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: "combining independent modules to solve multiple choice synonym and analogy problems"
  Abstract: Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best for all problem instances. Many researchers are examining ensemble methods that combine the output of successful, separately developed modules to create more accurate solutions. This paper examines three merging rules for combining probability distributions: the well known mixture rule, the logarithmic rule, and a novel product rule. These rules were applied with state-of-the-art results to two problems commonly used to assess human mastery of lexical semantics|synonym questions and analogy questions. All three merging rules result in ensembles that are more accurate than any of their component modules. The dierences among the three rules are not statistically signicant, but it is suggestive that the popular mixture rule is not the best rule for either of the two problems.

Connected through hyperedge HE-1 (induced by source paper "using the web as an implicit training set application to noun compound syntax and semantics"):
  with members:
    - "expressing implicit semantic relations without supervision"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "measuring semantic similarity by latent relational analysis"

Connected through hyperedge HE-2 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "evaluation of croatian word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "issues in evaluating semantic spaces using word analogies"

Connected through hyperedge HE-3 (induced by source paper "compositional approaches for representing relations between words a comparative study"):
  with members:
    - "issues in evaluating semantic spaces using word analogies"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "holographic embeddings of knowledge graphs"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"

Connected through hyperedge HE-4 (induced by source paper "semantic representations of word senses and concepts"):
  with members:
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "a large scale multilingual disambiguation of glosses"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "do multi sense embeddings improve natural language understanding"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-5 (induced by source paper "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a latent variable model approach to pmi based word embeddings"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "incorporating both distributional and relational semantics in word representations"
    - "distributional semantics beyond words supervised learning of analogy and paraphrase"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "grounded discovery of coordinate term relationships between software entities"):
  with members:
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-7 (induced by source paper "distributional semantics beyond words supervised learning of analogy and paraphrase"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "domain and function a dual space model of semantic relations and compositions"
    - "a context theoretic framework for compositionality in distributional semantics"
    - "from frequency to meaning vector space models of semantics"
    - "a survey of paraphrasing and textual entailment methods"
    - "bagpack a general framework to represent semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "the latent relation mapping engine algorithm and experiments"

Connected through hyperedge HE-8 (induced by source paper "domain and function a dual space model of semantic relations and compositions"):
  with members:
    - "experimenting with transitive verbs in a discocat"
    - "from frequency to meaning vector space models of semantics"
    - "bagpack a general framework to represent semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "the latent relation mapping engine algorithm and experiments"
    - "expressing implicit semantic relations without supervision"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"

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)