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: Un muy bien escrito e interesante artículo. Habría sido interesante incluir los algoritmos originales y los propuestos de forma más rigorosa (paso 1, paso 2, etc.) así como un análisis estadístico de la calidad de las respuestas de ambos.   Citar en el texto la figura 4.  Mejorar la calidad de las figuras 6 y 7.   En algunas partes coloca ecuación (n), en otras ec. (n) y en otras ecuación ec. (n)... padronzar para el primero o el segundo.  Padronizar también el nombre de los autores en las referencias bibliográficas, pues algunos usan iniciales, en otro los nombres completos y en otros mixtos.
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 comes up with a novel approach to searching the space of architectures for deep neural networks using reinforcement learning. The idea is straightforward and sensible: use a reinforcement learning strategy to iteratively grow a deep net graph (the space of actions is e.g. adding different layer types) via Q-learning. The reviewers agree that the idea is interesting, novel and promising but are underwhelmed with the execution of the experiments and the empirical results.     The idea behind the paper and the formulation of the problem are quite similar to a concurrent submission (
Example Output:
en