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: In this paper, the authors explicitly design geometrical structure into a CNN by combining it with a Scattering network. This aids stability and limited-data performance. The paper is well written, the contribution of combining Scattering and CNNs is novel and the results seem promising. I feel that such work was a missing piece in the Scattering literature to make it useful for practical applications.  I wish the authors would have investigated the effect of the stable bottom layers with respect to adversarial examples. This can be done in a relatively straightforward way with software like cleverhans [1] or deep fool [2]. It would be very interesting if the first layer's stability in the hybrid architectures increases robustness significantly, as this would tell us that these fooling images are related to low-level geometry. Finding that this is not the case, would be very interesting as well.  Further, the proposed architecture is not evaluated on real limited data problems. This would further strengthen the improved generalization claim. However, I admit that the Cifar-100 / Cifar-10 difference already seems like a promising indicator in this regard.  If one of the two points above will be addressed in an additional experiment, I would be happy to raise my score from 6 to 7.  Summary:   + An interesting approach is presented that might be useful for real-world limited data scenarios. + Limited data results look promising. - Adversarial examples are not investigated in the experimental section. - No realistic small-data problem is addressed.  Minor: - The authors should add a SOTA ResNet to Table 3, as NiN is indeed out of fashion these days. - Some typos: tacke, developping, learni.  [1]
Output: 

 en