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].

Let me give you an example: 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.
The answer to this example can be: es
Here is why: This review is written in spanish. Hence, the label is 'es'.

OK. solve this:
This paper aims to mine explicit rules from KB embedding space, and casts it into a sparse reconstruction problem. Experiments demonstrate its ability of extracting reasonable rules on a few link prediction datasets. The solution part sounds plausible. However, it confuses me that why we need to mine rules from learned KB embeddings. - It is still unclear what information these KB embeddings encode and it looks strange that we aim to learn rules including negation / disjunction from them. - If the goal is to extract useful rules (for other applications), it is necessary to compare it to "graph random walk" (
Answer:
en