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: __Note__: An earlier version of the review (almost identical to the present one) for an earlier version of the paper (available on arXiV) can be found here:
Example Output: en

Example Input: El artículo presenta una implementación simple de una BD para redes espaciales desarrollada con un SGBD open source. Describe previamente, de manera clara y adecuada la tecnología  y conceptos utilizados en este contexto de las redes espaciales. Plantea una metodología estándar para el desarrollo de una BD y modelo E-R simple (Nota: en la figura 2 está mal configurado el texto). Luego describe las tablas generadas por el modelo indicando los tipos de archivos utilizados. Continúa señalando que se implementó el algoritmo de búsqueda INE pero no existe evidencia de esto y de su uso. Finalmente señala en la evaluación, que esta etapa no se pudo llevar a cabo. En las conclusiones no se expresa ningún tipo y sólo se limita a resumir el trabajo realizado. En síntesis, un trabajo simple de diseño de BD que no proporciona un aporte científico, sólo una implementación útil y novedosa de redes espaciales. Se recomienda potenciar aspectos que puedan entregar un aporte, como evaluar distintas tecnologías de BDE, desempeño del algoritmo de búsqueda INE v/s otros, etc. Existen errores de acentuación de algunas palabras a lo largo del texto.
Example Output: es

Example Input: This paper introduces a maximum total correlation procedure, adds a target and then adds noise perturbations.  Technical issues:  The move from (1) to (2) is problematic. Yes it is a lower bound, but by igoring H(Z), equation (2) ignores the fact that H(Z) will potentially vary more significantly that H(Z|Y). As a result of removing H(Z), the objective (2) encourages Z that are low entropy as the H(Z) term is ignored, doubly so as low entropy Z results in low entropy Z|Y. Yes the -H(X|Z) mitigates against a complete entropy collapse for H(Z), but it still neglects critical terms. In fact one might wonder if this is the reason that semantic noise addition needs to be done anyway, just to push up the entropy of Z to stop it reducing too much.  In (3) arbitrary balancing paramters lamda_1 and lambda_2 are introduced ex-nihilo - they were not there in (2). This is not ever justified.  Then in (5), a further choice is made by simply adding L_{NLL} to the objective. But in the supervised case, the targets are known and so turn up in H(Z|Y). Hence now H(Z|Y) should be conditioned on the targets. However instead another objective is added again without justification, and the conditional entropy of Z is left disconnected from the data it is to be conditioned on. One might argue the C(X,Y,Z) simply acts as a prior on the networks (and hence implicitly on the weights) that we consider, which is then combined with a likelihood term, but this case is not made. In fact there is no explicit probabilistic or information theoretic motivation for the chosen objective.  Given these issues, it is then not too surprising that some further things need to be done, such as semantic noise addition to actually get things working properly. It may be the form of noise addition is a good idea, but given the troublesome objective being used in the first place, it is very hard to draw conclusions.  In summary, substantially better theoretical justification of the chosen model is needed, before any reasonable conclusion on the semantic noise modelling can be made.
Example Output:
en