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 artículo está muy bien escrito y se lee con facilidad, teniendo básicamente características de un artículo de divulgación.  Se discuten aspectos centrados esencialmente en los problemas de recuperación de información ante el crecimiento exponencial de los datos. La propuesta realizada al final del artículo es interesante, aunque, desde mi punto de vista la inclusión del tema Big Data está dado con cierta liviandad y requiere un análisis más detallado de lo que se describe.
Example Output: es

Example Input: - Strengths: The idea to investigate the types of relations between lexical items is very interesting and challenging. The authors make a good argument why going beyond analogy testing makes sense.    - Weaknesses: The paper does not justify or otherwise contextualize the choice of clustering for evaluation, rather than using a classification task, despite the fact that classification tasks are more straightforward to evaluate. No attempt is being made to explain the overall level of the results. How well would humans do on this task (given only the words, no context)?  - General Discussion:  I have read the authors' response.
Example Output: en

Example Input: The paper introduces a segmentation procedure for breast tissue images using SVM.  General comments:  a) Concerning the proposed approach: The segmentation method introduced only considers local color/gray value information. Perhaps introducing other features related to the membrane's local (e.g. the membrane has symmetry properties that can be exploited) or global (e.g. continuity, approximately circular shape) properties may improve the segmentation results.  Feature selection is also a key component to improve performance. Use a feature selection procedure.  b) Concerning the experimentation: You should describe the experiments with sufficient detail so others can replicate them: a) Make your data publicly available b) Explain how you have selected the classifier's parameters (parameter selection in an SVM is critical and may improve notably the performance).  You should join Figs 10 and 9 in a single plot so we can better see the differences.  You only provide qualitative segmentation results (Figs. 12, 14, 15), but quantitative results are also very important. Specially when it comes to comparisons. You should quantitatively compare your algorithm with other competing works in the literature.  c) Concerning paper organization: The paper is well written and clearly introduces the problem, describes the relevant literature and presents the solution. I have a few comments concerning the paper organization: a) Problem statement and literature review (intro and theoretical framework sections) are in my opinion a bit long, compared to other important sections, e.g. feature extraction and experimentation which are just one paragraph each. b) Although English is in general correct, there are some small mistakes that could be solved by having it read by a professional. c) There are two "proposed method" sections  Well written paper. Good literature review.  It only shows very preliminary results.
Example Output:
en