human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a simple and efficient method to generate word sense representations"
  Abstract: Distributed representations of words have boosted the performance of many Natural Language Processing tasks. However, usually only one representation per word is obtained, not acknowledging the fact that some words have multiple meanings. This has a negative effect on the individual word representations and the language model as a whole. In this paper we present a simple model that enables recent techniques for building word vectors to represent distinct senses of polysemic words. In our assessment of this model we show that it is able to effectively discriminate between words' senses and to do so in a computationally efficient manner.

Connected through hyperedge HE-1 (induced by source paper "watset local global graph clustering with applications in sense and frame induction"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "unsupervised semantic frame induction using triclustering"
    - "a framework for enriching lexical semantic resources with distributional semantics"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "improving hypernymy extraction with distributional semantic classes"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "human and machine judgements for russian semantic relatedness"

Connected through hyperedge HE-2 (induced by source paper "real multi sense or pseudo multi sense an approach to improve word representation"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "do multi sense embeddings improve natural language understanding"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-3 (induced by source paper "do multi sense embeddings improve natural language understanding"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "improved semantic representations from tree structured long short term memory networks"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"
    - "thumbs up sentiment classification using machine learning techniques"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CL(Computation and Language)