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 "the cqc algorithm cycling in graphs to semantically enrich and enhance a bilingual dictionary"):
  Abstract: Bilingual machine-readable dictionaries are knowledge resources useful in many automatic tasks. However, compared to monolingual computational lexicons like WordNet, bilingual dictionaries typically provide a lower amount of structured information such as lexical and semantic relations, and often do not cover the entire range of possible translations for a word of interest. In this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the automated disambiguation of ambiguous translations in the lexical entries of a bilingual machine-readable dictionary. The dictionary is represented as a graph, and cyclic patterns are sought in this graph to assign an appropriate sense tag to each translation in a lexical entry. Further, we use the algorithm's output to improve the quality of the dictionary itself, by suggesting accurate solutions to structural problems such as misalignments, partial alignments and missing entries. Finally, we successfully apply CQC to the task of synonym extraction.

Includes members:
  Member "roget s thesaurus and semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning")
  Member "wikipedia based semantic interpretation for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "eliminating search intent bias in learning to rank")
  Member "a uniform approach to analogies synonyms antonyms and associations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural latent relational analysis to capture lexical semantic relations in a vector space")
  Member "enriching a text by semantic disambiguation for information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a derivational rephrasing experiment for question answering")
  Member "combining independent modules to solve multiple choice synonym and analogy problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using the web as an implicit training set application to noun compound syntax and semantics")

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)