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 "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia"):
  Abstract: The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io/.

Includes members:
  Member "ernie enhanced language representation with informative entities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "yelm end to end contextualized entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia")
  Member "neural attentive bag of entities model for text classification":
    also connected through other hyperedges:
      (none)
  Member "studio ousia s quiz bowl question answering system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quizbowl the case for incremental question answering")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "representation learning of entities and documents from knowledge base descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "representation learning for words and entities")
  Member "learning distributed representations of texts and entities from knowledge base":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning cross context entity representations from text")
  Member "named entity disambiguation for noisy text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural attentive bag of entities model for text classification")

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)