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: Problemas de forma identificados: - Figura 7 no es clara en su contenido. - Figura 5 y 6 no aportan al trabajo desarrollado. - El proceso de transaccion deberia estar numerado. - Cuadro deberia ser Tabla, ademas la tabla 2, 3 y 4 no son legibles. Se deberian re-escribir. - Pagina 3 el texto de la palabra Samsung no es del mismo tipo de fuente. - Figura 8 no tiene titulo. - Se debe corregir y mejorar la ortografia (conclusiones). Problemas de fondo: - El apartado 3.1.2 no se encuentra relacionado directamente con el problematica planteada. - La descripcion de las arquitecturas y modalidades de utilizacion de NFC no aporta a la solucion expuesta. - En la seccion 4.1 se hace mencion a la maquina virtual Dalvik, sin embargo, desde la version de Android Lollipop hacia arriba, se utiliza ART y no Dalvik. - La tabla 6 no aporta el trabajo desarrollado. - El trabajo desarrollado utiliza 8 de 11 paginas en introduccion y tecnologias, sin embargo, solo se utiliza 1 pagina para mostrar las evidencias. - Se plantean algunos indicadores, sin embargo, no se da evidencia de los valores obtenidos. - Las figuras 10, 11 y 12 muestran capturas de la aplicacion?, sin embargo, no se indican en detalle la forma de aplicacion de las tecnologias en Android. - Falta evidencias de la aplicacion descrita, en particular desde al area de la ingenieria de software.
Example Output: es

Example Input: # Summary This paper presents an empirical study to identify a latent dimension of sentiment in word embeddings. # Strengths S1) Tackles a challenging problem of unsupervised sentiment analysis. S2) Figure 2, in particular, is a nice visualisation. # Weaknesses W1) The experiments, in particular, are very thin. I would recommend also measuring F1 performance and expanding the number of techniques compared. W2) The methodology description needs more organisation and elaboration. The ideas tested are itemised, but insufficiently justified. W3) The results are quite weak in terms of the reported accuracy and depth of analysis. Perhaps this work needs more development, particularly with validating the central assumption that the Distributional Hypothesis implies that opposite words, although semantically similar, are separated well in the vector space?
Example Output: en

Example Input: - Strengths: * Elaborate evaluation data creation and evaluation scheme. * Range of compared techniques: baseline/simple/complex - Weaknesses: * No in-depth analysis beyond overall evaluation results. - General Discussion: This paper compares several techniques for robust HPSG parsing. Since the main contribution of the paper is not a novel parsing technique but the empirical evaluation, I would like to see a more in-depth analysis of the results summarized in Table 1 and 2. It would be nice to show some representative example sentences and sketches of its analyses, on which the compared methods behaved differently. Please add EDM precision and recall figures to Table 2. The EDM F1 score is a result of a mixed effects of (overall and partial) coverage, parse ranking, efficiency of search, etc. The overall coverage figures in Table 1 are helpful but addition of EDM recall to Table 2 would make the situations clearer. Minor comment: - Is 'pacnv+ut' in Table 1 and 2 the same as 'pacnv' described in 3.4.3?
Example Output:
en