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: This paper presents a generative model of video sequence data where the frames are assumed to be generated by a static background with a 2d sprite composited onto it at each timestep.  The sprite itself is allowed to dynamically change its appearance and location within the image from frame to frame.  This paper follows the VAE (Variational Autoencoder) approach, where a recognition/inference network allows them to recover the latent state at each timestep.  Some results are presented on simple synthetic data (such as a moving rectangle on a black background or the “Moving MNIST” data.  However, the results are preliminary and I suspect that the assumptions used in the paper are far too strong too be useful in real videos.  On the Moving MNIST data, the numerical results are not competitive to state of the art numbers.  The model itself is also not particularly novel and the work currently misses some relevant citations.  The form of the forward model, for example, could be viewed as a variation on the DRAW paper by Gregor et al (ICML 2014).  Efficient Inference in Occlusion-Aware Generative Models of Images by Huang & Murphy (ICLR) is another relevant work, which used a variational auto-encoder with a spatial transformer and an RNN-like sequence model to model the appearance of multiple sprites on a background.  Finally, the exposition in this paper is short on many details and I don’t believe that the paper is reproducible from the text alone.  For example, it is not clear what the form of the recognition model is…  Low-level details (which are very important) are also not presented, such as initialization strategy.
Output: 

 en