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].

[Q]: 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?
[A]: en


[Q]: This paper shows:    1. Easy, constructive proofs to derive e-error upper-bounds on neural networks with O(log 1/e) layers and O(log 1/e) ReLU units.   2. Extensions of the previous results to more general function classes, such as smooth or vector-valued functions.   3. Lower bounds on the neural network size, as a function of its number of layers. The lower bound reveals the need of exponentially many more units to approximate functions using shallow architectures.  The paper is well written and easy to follow. The technical content, including the proofs in the Appendix, look correct. Although the proof techniques are simple (and are sometimes modifications of arguments by Gil, Telgarsky, or Dasgupta), they are brought together in a coherent manner to produce sharp results. Therefore, I am leaning toward acceptance.
[A]: en


[Q]: No hay contribución científica interesante ya que es la aplicación de una tecnología ya conocida a un caso de estudio. A pesar de que el trabajo está bien escrito técnicamente y es fácil de seguir no veo cuál es la pregunta de investigación que pretende responder. No se identifica un problema científico a solucionar y la propuesta es simple y poco original..
[A]:
es