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].

Example input: Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Example output: es
Example explanation: This review is written in spanish. Hence, the label is 'es'.
Q: This paper combines variational RNN (VRNN) and domain adversarial networks (DANN) for domain adaptation in the sequence modelling domain. The VRNN is used to learn representations for sequential data, which is the hidden states of the last time step. The DANN is used to make the representations domain invariant, therefore achieving cross domain adaptation. Experiments are done on a number of data sets, and the proposed method (VRADA) outperforms baselines including DANN, VFAE and R-DANN on almost all of them. I don't have questions about the proposed model, the model is quite clear and seems to be a simple combination of VRNN and DANN. But a few questions came up during the pre-review question phase: - As the authors have mentioned, DANN in general outperforms MMD based methods, however, the VFAE method which is based on MMD regularization on the representations seems to outperform DANN across the board. That seems to indicate VRNN + MMD should also be a good combination. - One baseline the authors showed in the experiments is R-DANN, which is an RNN version of DANN. There are two differences between R-DANN and VRADA: (1) R-DANN uses deterministic RNN for representation learning, while VRADA uses variational RNN; (2) on target domain R-DANN only optimizes adversarial loss, while VRADA optimizes both adversarial loss and reconstruction loss for feature learning. It would be good to analyze further where the performance gain comes from.
A:
en