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].

Ex Input:
The authors propose to sample from VAEs through a Markov chain [z_t ~ q(z|x=x_{t-1}), x_t ~ p(x|z=z_t)]. The paper uses confusing notation, oversells the novelty, ignoring some relevant previous results. The qualitative difference between regular sampling and this Gibbs chain is not very convincing, judging from the figures. It would be a great workshop paper (perhaps more), if the authors fix the notation, fix the discussion to related work, and produce more convincing (perhaps simply upscaled?) figures.  Comments:   - Rezende et al's (2014) original VAE paper already discusses the Markov chain, which is ignored in this paper  - Notation is nonstandard / confusing. At page 1, it’s unclear what the authors mean with “p(x|z) which is approximated as q(x|z)”. - It’s also not clear what’s meant with q(z). At page 2, q(z) is called the learned distribution, while p(z) can in general also be a learned distribution. - It’s not true that it’s impossible to draw samples from q(z): one can sample x ~ q(x) from the dataset, then draw z ~ q(z|x). - It's not explained whether the analysis only applies to continuous observed spaces, or also discrete observed spaces - Figures 3 and 4 are not very convincing.

Ex Output:
en


Ex Input:
The authors introduce a new memory model which allows memory access in O(log n) time.  Pros: * The paper is well written and everything is clear. * It's a new model and I'm not aware of a similar model. * It's clear that memory access time is an issue for longer sequences and it is clear how this model solves this problem.  Cons: * The motivation for O(log n) access time is to be able to use the model on very long sequences. While it is clear from the definition that the computation time is low because of its design, it is not clear that the model will really generalize well to very long sequences. * The model was also not tested on any real-world task.  I think such experiments should be added to show whether the model really works on long sequences and real-world tasks, otherwise it is not clear if this is a useful model.

Ex Output:
en


Ex Input:
- El artículo aborda un problema contingente y muy relevante, e incluye tanto un diagnóstico nacional de uso de buenas prácticas como una solución (buenas prácticas concretas). - El lenguaje es adecuado.  - El artículo se siente como la concatenación de tres artículos diferentes: (1) resultados de una encuesta, (2) buenas prácticas de seguridad, (3) incorporación de buenas prácticas. - El orden de las secciones sería mejor si refleja este orden (la versión revisada es #2, #1, #3). - El artículo no tiene validación de ningún tipo, ni siquiera por evaluación de expertos.

Ex Output:

es