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: "expressing implicit semantic relations without supervision"
  Abstract: We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patterns   is ranked according to how well each pattern Pi expresses the relations between X and Y. For example, given X=ostrich and Y=bird, the two highest ranking output patterns are "X is the largest Y" and "Y such as the X". The output patterns are intended to be useful for finding further pairs with the same relations, to support the construction of lexicons, ontologies, and semantic networks. The patterns are sorted by pertinence, where the pertinence of a pattern Pi for a word pair X:Y is the expected relational similarity between the given pair and typical pairs for Pi. The algorithm is empirically evaluated on two tasks, solving multiple-choice SAT word analogy questions and classifying semantic relations in noun-modifier pairs. On both tasks, the algorithm achieves state-of-the-art results, performing significantly better than several alternative pattern ranking algorithms, based on tf-idf.

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:
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "measuring semantic similarity by latent relational analysis"
    - "combining independent modules to solve multiple choice synonym and analogy problems"

Connected through hyperedge HE-2 (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-3 (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"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "combining independent modules to solve multiple choice synonym and analogy problems"

Connected through hyperedge HE-4 (induced by source paper "learning to predict from textual data"):
  with members:
    - "a survey of paraphrasing and textual entailment methods"
    - "learning sentence internal temporal 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)