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 "pre training of deep contextualized embeddings of words and entities for named entity disambiguation"):
  Abstract: Deep contextualized embeddings trained using unsupervised language modeling (e.g., ELMo and BERT) are successful in a wide range of NLP tasks. In this paper, we propose a new contextualized embedding model of words and entities for named entity disambiguation (NED). Our model is based on the bidirectional transformer encoder and produces contextualized embeddings for words and entities in the input text. The embeddings are trained using a new masked entity prediction task that aims to train the model by predicting randomly masked entities in entity-annotated texts. We trained the model using entity-annotated texts obtained from Wikipedia. We evaluated our model by addressing NED using a simple NED model based on the trained contextualized embeddings. As a result, we achieved state-of-the-art or competitive results on several standard NED datasets.

Includes members:
  Member "neural collective entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences")
  Member "improving entity linking by modeling latent relations between mentions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "collective entity disambiguation with structured gradient tree boosting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint learning of named entity recognition and entity linking")
  Member "bridging nonlinearities and stochastic regularizers with gaussian error linear units":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "xgpt cross modal generative pre training for image captioning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning distributed representations of texts and entities from knowledge base":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia")

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)