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 Input: Es un excelente trabajo de investigación de aplicación tecnológica, vinculada con una empresa de la zona, artículo muy apropiado para Infonor  Tiene fallas con respecto a un artículo tradicional, no hay trabajos relacionados, citaciones, referencias, etc. Tiene problemas de redacción, pero es claro en lo que presenta. Tiene frases largas y hay problemas de puntuación. Habría que adaptarlo para que pase la revisión de la revista.
Example Output: es

Example Input: The reviewers seem to agree that the framework presented is not very novel, something I agree with.  The experiments show that the low rank + diagonal parameterization can be useful, however. The paper could be improved by making a more tightened message, and clearer arguments. As it currently stands, however it does not seem ready for publication in ICLR.
Example Output: en

Example Input: This paper tests zoneout against a variety of datasets - character level, word level, and pMNIST classification - showing applicability in a wide range of scenarios. While zoneout acts as a regularizer to prevent overfitting, it also has similarities to residual connections. The continued analysis of this aspect, including analyzing how the gradient flow improves the given tasks, is of great interest and helps show it as an inherent property of zoneout.  This is a well written paper with a variety of experiments that support the claims. I have also previously used this technique in a recurrent setting and am confident on the positive impact it can have upon tasks. This is likely to become a standard technique used within RNNs across various frameworks.
Example Output:
en