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 "efficient graph based word sense induction by distributional inclusion vector embeddings"):
  Abstract: Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representations more interpretable. This paper proposes an accurate and efficient graph-based method for WSI that builds a global non-negative vector embedding basis (which are interpretable like topics) and clusters the basis indexes in the ego network of each polysemous word. By adopting distributional inclusion vector embeddings as our basis formation model, we avoid the expensive step of nearest neighbor search that plagues other graph-based methods without sacrificing the quality of sense clusters. Experiments on three datasets show that our proposed method produces similar or better sense clusters and embeddings compared with previous state-of-the-art methods while being significantly more efficient.

Includes members:
  Member "distributional inclusion vector embedding for unsupervised hypernymy detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inferring concept hierarchies from text corpora via hyperbolic embeddings")
  Member "a simple approach to learn polysemous word embeddings":
    also connected through other hyperedges:
      (none)
  Member "geometry of polysemy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings")
  Member "linear algebraic structure of word senses with applications to polysemy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  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 "that s sick dude automatic identification of word sense change across different timescales":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time out temporal referencing for robust modeling of lexical semantic change")

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)