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 "semantic measures for the comparison of units of language concepts or entities from text and knowledge base analysis"):
  Abstract: Semantic measures are widely used today to estimate the strength of the semantic relationship between elements of various types: units of language (e.g., words, sentences, documents), concepts or even instances semantically characterized (e.g., diseases, genes, geographical locations). Semantic measures play an important role to compare such elements according to semantic proxies: texts and knowledge representations, which support their meaning or describe their nature. Semantic measures are therefore essential for designing intelligent agents which will for example take advantage of semantic analysis to mimic human ability to compare abstract or concrete objects. This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses. Semantic measures generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance, which have been extensively studied by various communities over the last decades (e.g., Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few).

Includes members:
  Member "description and evaluation of semantic similarity measures approaches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic similarity from natural language and ontology analysis")
  Member "distributional measures as proxies for semantic relatedness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge based conversational search")
  Member "roget s thesaurus and semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning")
  Member "distributional measures of semantic distance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings")
  Member "a new similarity measure for taxonomy based on edge counting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memetic eda based approaches to comprehensive quality aware automated semantic web service composition")
  Member "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "thematically reinforced explicit semantic analysis")
  Member "serimi resource description similarity rdf instance matching and interlinking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an efficient metric of automatic weight generation for properties in instance matching technique")
  Member "hitting and commute times in large graphs are often misleading":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning networks from random walk based node similarities")

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.CL(Computation and Language)