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: "unsupervised keyphrase extraction with multipartite graphs"
  Abstract: We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.

Connected through hyperedge HE-1 (induced by source paper "large scale evaluation of keyphrase extraction models"):
  with members:
    - "kptimes a large scale dataset for keyphrase generation on news documents"
    - "keyphrase generation with correlation constraints"
    - "content selection in deep learning models of summarization"
    - "allennlp a deep semantic natural language processing platform"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "complex network based supervised keyword extractor"):
  with members:
    - "scake semantic connectivity aware keyword extraction"
    - "xgboost a scalable tree boosting system"
    - "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
    - "statistical keyword detection in literary corpora"
    - "smote synthetic minority over sampling technique"
    - "kea practical automatic keyphrase extraction"
    - "learning to extract keyphrases from text"

Connected through hyperedge HE-3 (induced by source paper "visual summarization of scholarly videos using word embeddings and keyphrase extraction"):
  with members:
    - "enriching word vectors with subword information"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "distributed representations of words and phrases and their compositionality"
    - "learning algorithms for keyphrase extraction"

Connected through hyperedge HE-4 (induced by source paper "scake semantic connectivity aware keyword extraction"):
  with members:
    - "keyword and keyphrase extraction using centrality measures on collocation networks"
    - "statistical keyword detection in literary corpora"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "learning algorithms for keyphrase extraction"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-5 (induced by source paper "inflo news categorization and keyphrase extraction for implementation in an aggregation system"):
  with members:
    - "regularizing and optimizing lstm language models"
    - "convolutional neural networks for sentence classification"

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)