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].

Ex Input:
- Strengths: The approach described in the manuscript outperformed the previous approaches and achieved the state-of-the-art result. Regarding data, the method used the combination of market and text data. The approach used word embeddings to define the weight of each lexicon term by extending it to the similar terms in the document. - Weaknesses: Deep-learning based methods were known to be able to achieve relatively good performances without much feature engineering in sentimental analysis. More literature search is needed to compare with the related works would be better. The approach generally improved performance by feature-based methods without much novelty in model or proposal of new features. - General Discussion: The manuscript described an approach in sentimental analysis. The method used a relatively new method of using word embeddings to define the weight of each lexicon term. However, the novelty is not significant enough.

Ex Output:
en

Ex Input:
El articulo esta muy bien escrito y se lee con facilidad, teniendo basicamente caracteristicas de un articulo de divulgacion. Se discuten aspectos centrados esencialmente en los problemas de recuperacion de informacion ante el crecimiento exponencial de los datos. La propuesta realizada al final del articulo es interesante, aunque, desde mi punto de vista la inclusion del tema Big Data esta dado con cierta liviandad y requiere un analisis mas detallado de lo que se describe.

Ex Output:
es

Ex Input:
This paper shows: 1. Easy, constructive proofs to derive e-error upper-bounds on neural networks with O(log 1/e) layers and O(log 1/e) ReLU units. 2. Extensions of the previous results to more general function classes, such as smooth or vector-valued functions. 3. Lower bounds on the neural network size, as a function of its number of layers. The lower bound reveals the need of exponentially many more units to approximate functions using shallow architectures. The paper is well written and easy to follow. The technical content, including the proofs in the Appendix, look correct. Although the proof techniques are simple (and are sometimes modifications of arguments by Gil, Telgarsky, or Dasgupta), they are brought together in a coherent manner to produce sharp results. Therefore, I am leaning toward acceptance.

Ex Output:
en