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 creates a physics simulator using theano, and uses it to learn a neural network policy by back propagating gradients through the simulation. The approach is novel, and is motivated by being able to learn policies for robotics.  My two key reservations with the paper are as follows: 1. The method is motivated by learning policies for robotics. However, the proposed method is *only* useful for robotics if the learned policy can transfer the real world. Transferring policies from simulation to real-world is an open research problem, and is particularly challenging with less realistic simulators. 2. They key novelty/benefit of this approach over other model-based approaches is that the simulator is differentiable. However, the only empirical comparison in the paper is to a model-free approach (CMA-ES). To appropriately demonstrate the approach, it should be compared to other model-based approaches, which do not require analytic derivatives of the model.  For the reader to fully understand the pros and cons of the approach, the paper should also include quantitative comparison between the speed of the proposed simulator, and that of standard simulation platforms.  Because the idea is interesting and novel, I think it lies above the acceptance threshold. However, it would be significantly improved with the aforementioned comparisons.  Lastly, the writing of the paper could be improved, as it is rather informal and/or imprecise in a number of places. Here are some examples: -- “we model the use of a neural network as a general controller for a robot” - can be more concisely phrased as something like “we model the robot controller using a neural network” or “the robot controller is modeled using a neural network" -- “In previous research, finding a gradient…” - This is a run-on sentence. -- “We basically jam this entire equation into” - This sentence is informal and imprecise. -- “deep learning neural network” - the word “learning” should be omitted -- “one big RNN, where we unfold over time” - should be “…RNN, which we unfold over time” or “…RNN, unfolded over time”  The writing would also be improved by making it more concise and fitting the paper into 8 pages.
Output: 

 en