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: I reviewed the manuscript as of December 6th.  Summary: The authors build upon generative adversarial networks for the purpose of steganalysis -- i.e. detecting hidden messages in a payload. The authors describe a new model architecture in which a new element, a 'steganalyser' is added a training objective to the GAN model.  Major Comments: The authors introduce an interesting new direction for applying generative networks. That said, I think the premise of the paper could stand some additional exposition. How exactly would a SGAN method be employed? This is not clear from the paper. Why does the model require a generative model? Steganalysis by itself seems like a classification problem (i.e. a binary decision if there a hidden message?) Would you envision that a user has a message to send and does not care about the image (container) that it is being sent with? Or does the user have an image and the network generates a synthetic version of the image as a container and then hide the message in the container? Or is the SGAN somehow trained as a method for detecting hidden codes performed by any algorithm in an image? Explicitly describing the use-case would help with interpreting the results in the paper.  Additionally, the experiments and analysis in this paper is quite light as the authors only report a few steganalysis performance numbers in the tables (Table 1,2,3). A more extensive analysis seems warranted to explore the parameter space and provide a quantitative comparison with other methods discussed (e.g. HUGO, WOW, LSB, etc.) When is it appropriate to use this method over the others? Why does the seed effect the quality of results? Does a fixed seed correspond realistic scenario for employing this method?  Minor comments: - Is Figure 1 necessary? - Why does the seed value effect the quality of the predictive performance of the model?
Output: 

 en