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: "wordnet2vec corpora agnostic word vectorization method"
  Abstract: A complex nature of big data resources demands new methods for structuring especially for textual content. WordNet is a good knowledge source for comprehensive abstraction of natural language as its good implementations exist for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism WordNet2Vec is proposed in the paper. It creates vectors for each word from WordNet. These vectors encapsulate general position - role of a given word towards all other words in the natural language. Any list or set of such vectors contains knowledge about the context of its component within the whole language. Such word representation can be easily applied to many analytic tasks like classification or clustering. The usefulness of the WordNet2Vec method was demonstrated in sentiment analysis, i.e. classification with transfer learning for the real Amazon opinion textual dataset.

Connected through hyperedge HE-1 (induced by source paper "comprehensive analysis of aspect term extraction methods using various text embeddings"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "advances in pre training distributed word representations"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "long term memory networks for question answering"

Connected through hyperedge HE-2 (induced by source paper "implanting rational knowledge into distributed representation at morpheme level"):
  with members:
    - "incorporating glosses into neural word sense disambiguation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "analysis of group evolution prediction in complex networks"):
  with members:
    - "a comparative study of different approaches for tracking communities in evolving social networks"
    - "community discovery in dynamic networks a survey"
    - "predicting community evolution in social networks"
    - "estimating continuous distributions in bayesian classifiers"
    - "different approaches to community evolution prediction in blogosphere"
    - "ged the method for group evolution discovery in social networks"
    - "identification of group changes in blogosphere"
    - "predicting group evolution in the social network"

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)