Definition: 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].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
Input: First, I'd like to thank the authors for their answers and clarifications. I find, the presentation of the multi-stage version of the model much clearer now.  Pros:  + The paper states a sparse coding problem using cosine loss, which allows to solve the problem in a single pass.  + The energy-based formulation allows bi-directional coding that incorporates top-down and bottom-up information in the feature extraction process.   Cons:  + The cost of running the evaluation could be large in the  multi-class setting, rendering the approach less attractive and the computational cost comparable to recurrent architectures.  + While the model is competitive and improves over the baseline, the paper would be more convincing with other comparisons (see text). The experimental evaluation is limited (a single database and a single baseline)  ------  The motivation of the sparse coding scheme is to perform inference in a feed forward manner. This property does not hold in the multi stage setting, thus optimization would be required (as clarified by the authors).  Having an efficient way of performing a bi-directional coding scheme is very interesting. As the authors clarified, this could not necessarily be the case, as the model needs to be evaluated many times for performing a classification.  Maybe an interesting combination would be to run the model without any class-specific bias, and evaluation only the top K predictions with the energy-based setting.  Having said this, it would be good to include a discussion (if not direct comparisons) of the trade-offs of using a model as the one proposed by Cao et al. Eg. computational costs, performance.  Using the bidirectional coding only on the top layers seems reasonable: one can get a good low level representation in a class agnostic way. This, however could be studied in more detail, for instance showing empirically the trade offs. If I understand correctly, now only one setting is being reported.  Finally, the authors mention that one benefit of using the architecture derived from the proposed coding method is the spherical normalization scheme, which can lead to smoother optimization dynamics. Does the baseline (or model) use batch-normalization? If not, seems relevant to test.   Minor comments:  I find figure 2 (d) confusing. I would not plot this setting as it does not lead to a function (as the authors state in the text).
Output: 

 en