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 "cluse cross lingual unsupervised sense embeddings"):
  Abstract: This paper proposes a modularized sense induction and representation learning model that jointly learns bilingual sense embeddings that align well in the vector space, where the cross-lingual signal in the English-Chinese parallel corpus is exploited to capture the collocation and distributed characteristics in the language pair. The model is evaluated on the Stanford Contextual Word Similarity (SCWS) dataset to ensure the quality of monolingual sense embeddings. In addition, we introduce Bilingual Contextual Word Similarity (BCWS), a large and high-quality dataset for evaluating cross-lingual sense embeddings, which is the first attempt of measuring whether the learned embeddings are indeed aligned well in the vector space. The proposed approach shows the superior quality of sense embeddings evaluated in both monolingual and bilingual spaces.

Includes members:
  Member "beyond bilingual multi sense word embeddings using multilingual context":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards learning cross modal perception trace models")
  Member "word translation without parallel data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "muse modularizing unsupervised sense embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross topic distributional semantic representations via unsupervised mappings")
  Member "breaking sticks and ambiguities with adaptive skip gram":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "word sense disambiguation for 158 languages using word embeddings only")
  Member "efficient non parametric estimation of multiple embeddings per word in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "do multi sense embeddings improve natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks")
  Member "learning bilingual word representations by marginalizing alignments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised cross lingual transfer of word embedding spaces")
  Member "multilingual models for compositional distributed semantics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised sentiment analysis for code mixed data")

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)