Given the task definition, example input & output, solve the new input case.
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].
Example: Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Output: es
This review is written in spanish. Hence, the label is 'es'.

New input case for you: The responses to the pre-review questions are not strong; especially w.r.t. the question about dataset density and why the dataset had to be subsampled, the authors responded that subsampling is common in recommender systems work, including the papers cited. This isn't a particularly strong justification of why subsampling is a good idea, and in particular doesn't answer the question of "how would the results look without subsampling," which I think is a question that could easily have been answered directly. Especially given that the goal of dealing with the cold-start issue is so heavily emphasized in the paper, in seems odd to sample the data to reduce sparsity. Other than that, the pre-review questions seem to have been answered satisfactorily. The contribution of the paper is to propose user and item embedding methods, as a means of learning complex non-linear interactions between users and items. This is fairly similar to recent work on deep RS, though the network formulation has some differences. Overall this is an reasonably put together paper that makes a contribution in an important area, though there are still some shortcomings that should be addressed, namely: 1) The evaluation is unusual. Recall@M is the only result reported, though this is not usually an evaluation seen in recommender systems research. At the very least other performance measures (rmse or AUC) should be reported for completeness, even if the results are not strong 2) Given that the contribution is fairly simple (i.e., the "standard" recommender systems task, but with a new model) it's a shame that unusual data samples have to be taken. This should be a case where it's possible to report results against competing methods using *exactly* the same data they used, and exactly the same error measure, for the fairest comparison possible. Without the above it's hard to tell how much the performance improvements are really due to the method being better, versus the choice of datasets and the choice of loss functions.
Output:
en