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 "mining the web for lexical knowledge to improve keyphrase extraction learning from labeled and unlabeled data"):
  Abstract: A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the article’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases.

Includes members:
  Member "mining the web for synonyms pmi ir versus lsa on toefl":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of keyphrase extraction")
  Member "learning algorithms for keyphrase extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two huge title and keyword generation corpora of research articles")
  Member "contextual normalization applied to aircraft gas turbine engine diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the management of context sensitive features a review of strategies")
  Member "answering subcognitive turing test questions a reply to french":
    also connected through other hyperedges:
      (none)
  Member "kea practical automatic keyphrase extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "two huge title and keyword generation corpora of research articles")
  Member "extraction of keyphrases from text evaluation of four algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a new keyphrases extraction method based on suffix tree data structure for arabic documents clustering")
  Member "wrap up a trainable discourse module for information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning algorithms for keyphrase extraction")

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.LG(Machine Learning)