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: El articulo es un mero anuncio del trabajo de investigacion que sera realizado en una etapa posterior. Escasamente muestra el resumen del marco teorico, con solo algunas referencias y un insuficiente estudio del estado del arte sobre el tema en cuestion. Por consiguiente no hay resultados en base a los cuales establecer juicios. El titulo del articulo no guarda relacion a lo presentado en el cuerpo del informe. Presenta una gran cantidad de errores de redaccion, ortograficos y gramaticales, figuras ilegibles, referencia a figuras inexistentes, titulo de una tabla en otra columna, etc.
Example Output: es

Example Input: This paper presents a clear hierarchical taxonomy of transfer learning methods as applicable to sequence tagging problems. This contextualizes and unifies previous work on specific instances of this taxonomy. Moreover, the paper shows that previously unexplored places in this taxonomy are competitive with or superior to the state of the art in key benchmark problems. It'd be nice to see this explored further, such as highlighting what is the loss as you move from the more restrictive to the less restrictive transfer learning approaches, but I believe this paper is interesting and acceptable as-is.
Example Output: en

Example Input: Encouraging orthogonality in weight features has been reported useful for deep networks in many previous works. The authors present a explicit regularization cost to achieve de-correlation among weight features in a layer and encourage orthogonality. Further, they also show why and how negative correlations can and should be avoided for better de-correlation. Orthogonal weight features achieve better generalization in case of large number of trainable parameters and less training data, which usually results in over-fitting. As also mentioned by the authors biases help in de-correlation of feature responses even in the presence of correlated features (weights). Regularization techniques like OrthoReg can be more helpful in training deeper and leaner networks, where the representational capacity of each layer is low, and also generalize better. Although the improvement in performances is not significant the direction of research and the observations made are promising.
Example Output:
en