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:
The starting point of this work is the understanding that by having decorrelated neurons (e.g. neurons that only fire on background, or only on foreground regions) one provides independent pieces of information to the subsequent decisions. As such one gives "complementary viewpoints" of the input to the subsequent layers, which can be thought of as performing ensembling/expert combination within the model, rather than using an ensemble of networks. For this, the authors propose a sensible method to decorrelate the activations of intermediate neurons, with the aim of delivering complementary inputs to the final classification layers: they split intermediate neurons to a "foreground" and a "background" subset, and append side-losses that force them to be zero on background and foreground pixels respectively. They demonstrate that this can improve classification on a mid-scale classification example (a fraction of imagenet, and a ResNet with 18, rather than 150 layers), when compared to a "vanilla" baseline that does not use these losses. I enjoyed reading the paper because the idea is simple, smart, and seems to be effective. But there are a few concerns; -firstly, the way of doing this seems very particular to vision. In vision one knows that masking the features (during both training and testing) helps, e.g.

Ex Output:
en

Ex Input:
this paper applies an advanced algorithm to gait control, but is seems that more explanations are needed for a clearer understanding and comparison with other traditional algorithms. It depends on two another papers for its complete understanding. The math is very well stated but I was unable to see the advantages or purpose of that math regarding gait control. Also, it is not clear for the reader how fast or slow is the calculation time regarding task time.

Ex Output:
en

Ex Input:
Me parece un trabajo interesante, principalmente por aportar evidencia empirica sobre tecnicas de co-creacion. Su contenido y estructura estan tecnicamente bien presentados. No obstante, el trabajo tiene omisiones relevantes para valorar su contribucion cientifica. Por ejemplo, no presenta analisis estadistico de los datos arrojados. Solo expone una distribucion de los resultados sin evidenciar significancia estadistica. Tambien extrano una seccion (o en las conclusiones) sobre limitaciones a la validez del experimento. Creo que tiene varias amenazas a la validez que no han sido tratadas. Por nombrar algunas: no se explica si la asignacion a los grupos fue aleatoriamente; el tiempo de las sesiones es distinta; el aprendizaje en sesiones anteriores, etc. Otras sugerencias: pag. 1 Titulo Creo que no es adecuado el titulo. No representa el core del trabajo que es un experimento. pag. 4 Segunda columna Errores en formato de vinetas pag. 4 Tercera vineta segunda columna cada VEZ que pag. 5 Ultima vineta primera columna parentesis

Ex Output:
es