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: "keyphrase cloud generation of broadcast news"
  Abstract: This paper describes an enhanced automatic keyphrase extraction method applied to Broadcast News. The keyphrase extraction process is used to create a concept level for each news. On top of words resulting from a speech recognition system output and news indexation and it contributes to the generation of a tag/keyphrase cloud of the top news included in a Multimedia Monitoring Solution system for TV and Radio news/programs, running daily, and monitoring 12 TV channels and 4 Radios.

Connected through hyperedge HE-1 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-2 (induced by source paper "rakun rank based keyword extraction via unsupervised learning and meta vertex aggregation"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-3 (induced by source paper "summarization of films and documentaries based on subtitles and scripts"):
  with members:
    - "on the application of generic summarization algorithms to music"
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving important passage retrieval"):
  with members:
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "universal rate efficient scalar quantization"

Connected through hyperedge HE-5 (induced by source paper "hourly traffic prediction of news stories"):
  with members:
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"
    - "social information processing in news aggregation"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-6 (induced by source paper "key phrase extraction of lightly filtered broadcast news"):
  with members:
    - "a new approach to keyphrase extraction using neural networks"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-7 (induced by source paper "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"):
  with members:
    - "kea practical automatic keyphrase extraction"

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.IR(Information Retrieval)