package googlers;

import java.util.Scanner;
import java.util.PriorityQueue;
import java.util.Comparator;
public class Googlers {
	
	public static void main(String[] args) {
		
		int n,s,p,count=0,t;		
		Scanner sin=new Scanner(System.in);
		
		t=Integer.parseInt(sin.nextLine());
		
		
		int result[]=new int[t];
		int j=0;
		
		if(t>=1 && t<=100)
		{
		for (int k = 0; k < t; k++) {
		count=0;	
		
			
        String ip = sin.nextLine();
        
        String[]tokens=ip.split(" ");
		
		
		n=Integer.parseInt(tokens[0]);
		s=Integer.parseInt(tokens[1]);
		p=Integer.parseInt(tokens[2]);
		
		if( (s>=0 && s<=n) && (p>=0 && p<=10) )
		{
		int[] total=new int[n];
		
		
		for (int i = 0; i < n; i++)
        {
            total[i] = Integer.parseInt(tokens[i+3]);
        }
		
		Comparator comparator=new PointComparator();
		PriorityQueue pq=new PriorityQueue<Point>(1, comparator);
		
		for (int i = 0; i < n; i++)
		{
			
			int x=total[i]/3;
			int r=total[i]%3;
			if(x>=p)
				count++;
			else if(x<(p-2))
				continue;
			else
			{
				//System.out.println("enter "+x+" "+(p-2));
				if(p-x==1)
				{
					Point temp=new Point();
					temp.q=x;
					temp.r=r;
					pq.add(temp);
				}
				else  // p-x=2
				{
					if(r==0)
						continue;
					else
					{
						Point temp=new Point();
						temp.q=x;
						temp.r=r;
						pq.add(temp);
					}				
					
				}
					
			}
			//System.out.println("hi "+pq.size());
			
		}
		
		
		
		while(pq.size()!=0)
		{
			Point temp=(Point)pq.remove();
			if(p-temp.q==1 && temp.q!=0)
			{
				if(temp.r>=1)
					count++;
				else
				{
					if(s!=0)
					{
						s--;
						count++;
					}
				}
			}
			else if(p-temp.q==2)
			{
				if(s!=0 && (temp.q+temp.r)>=p)
				{
					s--;
					count++;
				}
			}
			
				
		}
		
		//System.out.println(p);
		
		result[j++]=count;
		
		}
		}
		
		for (int i = 0; i < t; i++) 
		{
			System.out.println("Case #"+(i+1)+": "+result[i]);
		}
		}
		
	}
        
        
		
		/*Point x=new Point();
		x.q=8;
		Point y=new Point();
		y.q=6;
		Point z=new Point();
		z.q=7;
		
		pq.add(x);
		pq.add(y);
		pq.add(z);
		*/
		
		
		
		
	

}

class PointComparator implements Comparator<Point>
{
    @Override
    public int compare(Point x, Point y)
    {
        // Assume neither string is null. Real code should
        // probably be more robust
        if (x.q < y.q)
        {
            return 1;
        }
        if (x.q  > y.q)
        {
            return -1;
        }
        return 0;
    }
}



class Point
{
	int q,r;

	public int getQ() {
		return q;
	}

	public void setQ(int q) {
		this.q = q;
	}

	public int getR() {
		return r;
	}

	public void setR(int r) {
		this.r = r;
	}
	
	
}

package fixjava;

import java.util.concurrent.Callable;
import java.util.concurrent.Future;

import fixjava.ParallelWorkQueue.CallableFactory;

public class ArrayStats {
	public final double[][] data;
	public final int n, k;

	private double[][] centered;
	private double[] mean;
	private double[][] cov;
	private double[] stddev = null;
	private double[][] corr = null;

	/** Calculate covariance matrix */
	public ArrayStats(double[][] data) {
		if (data == null || data.length < 2 || data[0].length < 2)
			throw new IllegalArgumentException("Invalid data");
		this.data = data;
		this.n = data.length;
		this.k = data[0].length;
	}

	/** Compute mean for each dimension */
	public double[] mean() {
		if (mean == null) {
			mean = new double[k];
			for (int dim = 0; dim < k; dim++) {
				double tot = 0;
				for (int pt = 0; pt < n; pt++)
					tot += data[pt][dim];
				mean[dim] = tot / n;
			}
		}
		return mean;
	}

	/** Compute stddev for each dimension */
	public double[] stddev() {
		if (stddev == null) {
			// Make sure means are computed
			mean();

			stddev = new double[k];
			for (int dim = 0; dim < k; dim++) {
				for (int pt = 0; pt < n; pt++) {
					double diff = data[pt][dim] - mean[dim];
					stddev[dim] += diff * diff;
				}
				stddev[dim] = Math.sqrt(stddev[dim] / n);
			}
		}
		return stddev;
	}

	/** Re-center data by subtracting mean from each dimension */
	public double[][] centered() {
		if (centered == null) {
			// Make sure means are computed
			mean();

			centered = new double[n][k];
			for (int pt = 0; pt < n; pt++)
				for (int dim = 0; dim < k; dim++)
					centered[pt][dim] = data[pt][dim] - mean[dim];
		}
		return centered;
	}

	/** Compute covariance matrix */
	public double[][] cov() {
		if (cov == null) {
			// Make sure centered version of data is computed
			centered();

			// Work on transposed array for cache coherence
			final double[][] centeredT = ArrayUtils.transpose(centered);

			cov = new double[k][k];

			try {
				//final DecimalFormat formatPercent1dp = new DecimalFormat("0.0%");
				for (Future<Void> res : new ParallelWorkQueue<Integer, Void>(/* numThreads = */25,
						ParallelWorkQueue.makeIntRangeIterable(k), new CallableFactory<Integer, Void>() {
							//IntegerMutable counter = new IntegerMutable();

							@Override
							public Callable<Void> newInstance(final Integer ii) {
								return new Callable<Void>() {
									int dim1 = ii.intValue();
									double[][] centeredTLocal = centeredT;
									double[][] covLocal = cov;

									@Override
									public Void call() {
										for (int dim2 = dim1; dim2 < k; dim2++) {
											double numer = 0.0;
											for (int pt = 0; pt < n; pt++)
												numer += centeredTLocal[dim1][pt] * centeredTLocal[dim2][pt];
											int denom = n - 1;
											covLocal[dim1][dim2] = covLocal[dim2][dim1] = denom == 0 ? 0 : numer / denom;
										}
										//										int count = counter.increment();
										//										if (count % 10 == 0)
										//											System.out.println(formatPercent1dp.format((count / (float) (k - 1))));
										return null;
									}
								};
							}
						}))
					// Barricade
					res.get();
			} catch (Exception e) {
				e.printStackTrace();
				System.exit(1);
			}
		}
		return cov;
	}

	/** Compute correlation matrix */
	public double[][] corr() {
		if (corr == null) {
			// Make sure per-dim stddevs and covariance matrix are computed
			stddev();
			cov();

			corr = new double[k][k];
			for (int dim1 = 0; dim1 < k; dim1++) {
				for (int dim2 = dim1; dim2 < k; dim2++) {
					double prod = stddev[dim1] * stddev[dim2];
					corr[dim1][dim2] = corr[dim2][dim1] = prod == 0.0 ? 0.0 : cov[dim1][dim2] / prod;
				}
			}
		}
		return corr;
	}
}
