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 tema de adopción de sistemas ERP se ha desarrollado los últimos 15 años, y en particular la revisión de factores que impliquen su éxito es un tema de investigación relevante. -	En el trabajo se desarrolló un análisis de la literatura ERP sobre el tema que aborda   factores críticos de éxito, metodologías de adopción y tamaño de la organización. -	Si bien es un error indicar que "últimamente se  ha generado un tercer tipo..." en relación a asociar fases del proyecto y FCE, pues hace más de 12 años esto se ha realizado, ver Cita [36] del mismo trabajo, no es un tema cerrado. -	Propongo a los autores el definir que es "éxito de adopción ERP", pues existe literatura en relación a ello muy interesante para sus fines últimos (ver trabajos de la profesora Markus). -	Metodológicamente existen tres limitaciones importantes a superar con miras a avanzar en una publicación científica de impacto. Primero, no se ha considerado el factor de impacto de la publicaciones, con lo cual se ha desestimado el desarrollo asociado a este tipo de revisiones, mezclando trabajos de alto impacto (ejemplo, artículos de revistas ISIs factor de impacto >2, como MISQ) con trabajos sin una revisión de calidad (ejemplo, lo que Scholar "lanza" sólo por coincidir en la búsqueda). Segundo, se dice que se tomarán referencias del 2008 en adelante prioritariamente, pero muchas de sus referencias  son de antes de esa fecha, y eso no es raro, pues entre 2001 y 2004 hay una explosión de trabajos en este ámbito específico. Tercero, no se han incluido explícitamente trabajos de conferencias del área de alto impacto en la comunidad científica como AMCIS o ECIS (la primera tiene una base de datos privada, no accesible por google y la segunda es libre). -	Las limitaciones antes expuestas restringen los alcances de los interesantes  resultados expuestos en el trabajo.
Example Output: es

Example Input: This paper describes a system to assist written test scoring.  - Strengths: The paper represents an application of an interesting NLP problem -- recognizing textual entailment -- to an important task -- written test scoring.  - Weaknesses: There isn't anything novel in the paper. It consist of an application of an existing technology to a known problem.  The approach described in the paper is not autonomous -- it still needs a human to do the actual scoring. The paper lacks any quantitative or qualitative evaluation of how useful such system is. That is, is it making the job of the scorer easier? Is the scorer more effective as compared to not having automatic score?  The system contains multiple components and it is unclear how the quality of each one of them contributes to the overall experience.  The paper needs more work with the writing. Language and style is rough in several places.  The paper also contains several detailed examples, which don't necessarily add a lot of value to the discussion.   For the evaluation of classification, what is the baseline of predicting the most frequent class?  - General Discussion: I find this paper not very inspiring. I don't see the message in the paper apart from announcing having build such a system
Example Output: en

Example Input: ResNet and other architectures that use shortcuts have shown empirical success in several domains and therefore, studying the optimization for such architectures is very valuable. This paper is an attempt to address some of the properties of networks that use shortcuts. Some of the experiments in the paper are interesting. However, there are two main issues with the current paper:  1- linear vs non-linear: I think studying linear networks is valuable but we should be careful not to extend the results to networks with non-linear activations without enough evidence. This is especially true for Hessian as the Hessian of non-linear networks have very large condition number (see the ICLR submission "Singularity of Hessian in Deep Learning") even in cases where the optimization is not challenging. Therefore, I don't agree with the claims in the paper on non-linear networks. Moreover, one plot on MNIST is not enough to claim that non-linear networks behave similar to linear networks.  2- Hessian at zero initial point: The explanation of why we should be interested in Hessain at zero initial point is not acceptable. The zero initial point is not interesting because it is a very particular point that cannot tell us about the Hessian during optimization.
Example Output:
en