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 "a short survey on sense annotated corpora for diverse languages and resources"):
  Abstract: With the advancement of research in word sense disambiguation and deep learning, large sense-annotated datasets are increasingly important for training supervised systems. However, gathering high-quality sense-annotated data for as many instances as possible is an arduous task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this paper we present an overview of currently available sense-annotated corpora, both manually and automatically constructed, for various languages and resources (i.e. WordNet, Wikipedia, BabelNet). General statistics and specific features of each sense-annotated dataset are also provided.

Includes members:
  Member "huge automatically extracted training sets for multilingual word sense disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a short survey on sense annotated corpora")
  Member "knowledge based word sense disambiguation using topic models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings")
  Member "shotgunwsd an unsupervised algorithm for global word sense disambiguation inspired by dna sequencing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vector of locally aggregated word embeddings vlawe a novel document level representation")
  Member "named entity disambiguation for noisy text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia")
  Member "semi supervised word sense disambiguation with neural models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "does bert make any sense interpretable word sense disambiguation with contextualized embeddings")
  Member "word sense disambiguation using a bidirectional lstm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "does bert make any sense interpretable word sense disambiguation with contextualized embeddings")

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)