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].

This paper proposes a recurrent architecture for simultaneously predicting motion and action states of agents. The paper is well written, clear in its presentation and backed up by good experiments. They demonstrate that by forcing the network to predict motion has beneficial consequences on the classification of actions states, allowing more accurate classification with less training data. They also show how the information learned by the network is interpretable and organised in a hierarchy.  Weaknesses: - a critical discussion on the interplay between motion an behaviour that is needed to experience the benefits of their proposed model is missing from the paper. - moreover, a discussion on how this approach could scale to more challenging scenarios "involving animals" and visual input for instance and more general "behaviours" is also missing; The criticism here is pointed at the fact that the title/abstract claim general behaviour modelling, whilst the experiments are focused on two very specific and relatively simple scenarios, making the original claim a little bit far fetched unless its backed up by additional evidence. Using "Insects", or "fruit flies" would be more appropriate than "animals".
Output:
en