You will be given a definition of a task first, then some input of the task.
In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

The idea of universality that is independent of input distribution and dimension, depending only on the algorithm is an appealing one. However, as an empirical study, this paper comes up somewhat short:  1. Exactly one algorithm is shown for the deep learning example. It would have been more convincing to compare distributions with one or more algorithms.   2. The definition (1),  and much of the work of Section 2.1 seems to have already been covered in Deift (2014), Section 1.3. In that paper, a number of different algorithms for the solution of linear systems are considered, and then the concept of universality becomes more plausible. I do not see enough of such algorithmic comparisons in this paper (same problem setup, different algorithms).  3.  It seems to me that what practitioners might care about in practice are both the mean and variance in running times; these quantities are buried in (1). So I question how useful the distribution itself might be for algorithm tuning.   At the least, many more empirical comparisons should be provided to convince me that the universality holds across a broad range of algorithms.
Output:
en