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: "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization"
  Abstract: Fast and effective automated indexing is critical for search and personalized services. Key phrases that consist of one or more words and represent the main concepts of the document are often used for the purpose of indexing. In this paper, we investigate the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction. These features include the use of signal words and freebase categories. Some of these features lead to significant improvements in the accuracy of the results. We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization. Light filtering removes sentences from the document, which are judged peripheral to its main content. Co-reference normalization unifies several written forms of the same named entity into a unique form. We also needed a "Gold Standard" - a set of labeled documents for training and evaluation. While the subjective nature of key phrase selection precludes a true "Gold Standard", we used Amazon's Mechanical Turk service to obtain a useful approximation. Our data indicates that the biggest improvements in performance were due to shallow semantic features, news categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of deeper semantic features such as Freebase sub-categories was not beneficial by itself, but in combination with pre-processing, did cause slight improvements in the nDCG scores.

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"
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "complex network based supervised keyword extractor"):
  with members:
    - "scake semantic connectivity aware keyword extraction"
    - "unsupervised keyphrase extraction with multipartite graphs"
    - "xgboost a scalable tree boosting system"
    - "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 "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-4 (induced by source paper "kptimes a large scale dataset for keyphrase generation on news documents"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "keyphrase generation with correlation constraints"
    - "content selection in deep learning models of summarization"
    - "deep keyphrase generation"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (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"
    - "keyphrase cloud generation of broadcast news"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"

Connected through hyperedge HE-6 (induced by source paper "extending a single document summarizer to multi document a hierarchical approach"):
  with members:
    - "privacy preserving important passage retrieval"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving important passage retrieval"):
  with members:
    - "keyphrase cloud generation of broadcast news"
    - "universal rate efficient scalar quantization"

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)