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: UPDATE: The authors addressed all my concerns in the new version of the paper, so I raised my score and now recommend acceptance. -------------- This paper combines the recent progress in variational autoencoder and autoregressive density modeling in the proposed PixelVAE model. The paper shows that it can match the NLL performance of a PixelCNN with a PixelVAE that has a much shallower PixelCNN decoder. I think the idea of capturing the global structure with a VAE and modeling the local structure with a PixelCNN decoder makes a lot of sense and can prevent the blurry reconstruction/samples of VAE. I specially like the hierarchical image generation experiments.  I have the following suggestions/concerns about the paper:  1) Is there any experiment showing that using the PixelCNN as the decoder of VAE will result in better disentangling of high-level factors of variations in the hidden code? For example, the authors can train a PixelVAE and VAE on MNIST with 2D hidden code and visualize the 2D hidden code for test images and color code each hidden code based on the digit and show that the digits have a better separation in the PixelVAE representation. A semi-supervised classification comparison between VAE and the PixelVAE will also significantly improve the quality of the paper.  2) A similar idea is also presented in a concurrent ICLR submission "Variational Lossy Autoencoder". It would be interesting to have a discussion included in the paper and compare these works.  3) The answer to the pre-review questions made the architecture details of the paper much more clear, but I still ask the authors to include the exact architecture details of all the experiments in the paper and/or open source the code. The clarity of the presentation is not satisfying and the experiments are difficult to reproduce.  4) As pointed out in my pre-review question, it would be great to include two sets of MNIST samples maybe in an appendix section. One with PixelCNN and the other with PixelVAE with the same pixelcnn depth to illustrate the hidden code in PixelVAE actually captures the global structure.  I will gladly raise the score if the authors address my concerns.
Output: 

 en