human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "yelm end to end contextualized entity linking"):
  with members:
    - "joint learning of named entity recognition and entity linking"
    - "end to end neural entity linking"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "attention is all you need"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"

Question: Please tell me which class the center node 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)