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: "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge"
  Abstract: We describe a new semantic relatedness measure combining the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. Our measure achieves the currently highest results on the WS-353 test: a Spearman rho coefficient of 0.79 (vs. 0.75 in (Gabrilovich and Markovitch, 2007)) when applying the measure directly, and a value of 0.87 (vs. 0.78 in (Agirre et al., 2009)) when using the prediction of a polynomial SVM classifier trained on our measure. #R##N#In the appendix we discuss the adaptation of ESA to 2011 Wikipedia data, as well as various unsuccessful attempts to enhance ESA by filtering at word, sentence, and section level.

Connected through hyperedge HE-1 (induced by source paper "thematically reinforced explicit semantic analysis"):
  with members:
    - "mining meaning from wikipedia"

Connected through hyperedge HE-2 (induced by source paper "semantic measures for the comparison of units of language concepts or entities from text and knowledge base analysis"):
  with members:
    - "description and evaluation of semantic similarity measures approaches"
    - "distributional measures as proxies for semantic relatedness"
    - "roget s thesaurus and semantic similarity"
    - "distributional measures of semantic distance a survey"
    - "a new similarity measure for taxonomy based on edge counting"
    - "serimi resource description similarity rdf instance matching and interlinking"
    - "hitting and commute times in large graphs are often misleading"
    - "effectively integrating information content and structural relationship to improve the go based similarity measure between proteins"

Connected through hyperedge HE-3 (induced by source paper "semantic sort a supervised approach to personalized semantic relatedness"):
  with members:
    - "roget s thesaurus and semantic similarity"
    - "active learning using smooth relative regret approximations with applications"
    - "text relatedness based on a word thesaurus"
    - "wikipedia based semantic interpretation for natural language processing"
    - "the google similarity distance"
    - "roget s thesaurus as a lexical resource for natural language processing"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "wikipedia arborification and stratified explicit semantic analysis"):
  with members:
    - "mining meaning from wikipedia"

Connected through hyperedge HE-5 (induced by source paper "query expansion term selection using the ewc semantic relatedness measure"):
  with members:
    - "comparing and combining methods for automatic query expansion"

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)