human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "complex network based supervised keyword extractor"):
  Abstract: Abstract   In this paper, we present a supervised framework for automatic keyword extraction from single document. We model the text as complex network, and construct the feature set by extracting select node properties from it. Several node properties have been exploited by unsupervised, graph-based keyword extraction methods to discriminate keywords from non-keywords. We exploit the complex interplay of node properties to design a supervised keyword extraction method.  The training set is created from the feature set by assigning a label to each candidate keyword depending on whether the candidate is listed as a gold-standard keyword or not. Since the number of keywords in a document is much less than non-keywords, the curated training set is naturally imbalanced. We train a binary classifier to predict keywords after balancing the training set.  The model is trained using two public datasets from scientific domain and tested using three unseen scientific corpora and one news corpus. Comparative study of the results with several recent keyword and keyphrase extraction methods establishes that the proposed method performs better in most cases. This substantiates our claim that graph-theoretic properties of words are effective discriminators between keywords and non-keywords. We support our argument by showing that the improved performance of the proposed method is statistically significant for all datasets. We also evaluate the effectiveness of the pre-trained model on Hindi and Assamese language documents. We observe that the model performs equally well for the cross-language text even though it was trained only on English language documents. This shows that the proposed method is independent of the domain, collection, and language of the training corpora.

Includes members:
  Member "scake semantic connectivity aware keyword extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a spark ml driven preprocessing approach for deep learning based scholarly data applications")
  Member "unsupervised keyphrase extraction with multipartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "supervised topical key phrase extraction of news stories using crowdsourcing light filtering and co reference normalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "statistical keyword detection in literary corpora":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scake semantic connectivity aware keyword extraction")
  Member "smote synthetic minority over sampling technique":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "kea practical automatic keyphrase extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "learning to extract keyphrases from text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two huge title and keyword generation corpora of research articles")

Question: Please tell me which class the source paper 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)