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: 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.
Example output: es
Example explanation: This review is written in spanish. Hence, the label is 'es'.
Q: - Strengths: This paper presents an extension of many popular methods for learning vector representations of text. The original methods, such as skip-gram with negative sampling, Glove, or other PMI based approaches currently use word cooccurrence statistics, but all of those approaches could be extended to n-gram based statistics. N-gram based statistics would increase the complexity of every algorithm because both the vocabulary of the embeddings and the context space would be many times larger. This paper presents a method to learn embeddings for ngrams with ngram context, and efficiently computes these embeddings. On similarity and analogy tasks, they present strong results. - Weaknesses: I would have loved to see some experiments on real tasks where these embeddings are used as input beyond the experiments presented in the paper. That would have made the paper far stronger. - General Discussion: Even with the aforementioned weakness, I think this is a nice paper to have at ACL. I have read the author response.
A:
en