human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "you shall know the most frequent sense by the company it keeps"):
  Abstract: Identification of the most frequent sense of a polysemous word is an important semantic task. We introduce two concepts that can benefit MFS detection: companions, which are the most frequently co-occurring words, and the most frequent translation in a bitext. We present two novel methods that incorporate these new concepts, and show that they advance the state of the art on MFS detection.

Includes members:
  Member "de conflated semantic representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using multi sense vector embeddings for reverse dictionaries")
  Member "bilingual learning of multi sense embeddings with discrete autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a variational approach to weakly supervised document level multi aspect sentiment classification")
  Member "embedding words and senses together via joint knowledge enhanced training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings")
  Member "linear algebraic structure of word senses with applications to polysemy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "autoextend extending word embeddings to embeddings for synsets and lexemes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "exploiting similarities among languages for machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)