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 uniform approach to analogies synonyms antonyms and associations"
  Abstract: Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.

Connected through hyperedge HE-1 (induced by source paper "neural latent relational analysis to capture lexical semantic relations in a vector space"):
  with members:
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "analogical inference for multi relational embeddings"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "complex embeddings for simple link prediction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "holographic embeddings of knowledge graphs"
    - "embedding semantic relations into word representations"

Connected through hyperedge HE-2 (induced by source paper "analogical reasoning on chinese morphological and semantic relations"):
  with members:
    - "learning chinese word representations from glyphs of characters"
    - "enriching word vectors with subword information"
    - "bagpack a general framework to represent semantic relations"

Connected through hyperedge HE-3 (induced by source paper "insights into analogy completion from the biomedical domain"):
  with members:
    - "issues in evaluating semantic spaces using word analogies"
    - "towards universal paraphrastic sentence embeddings"
    - "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"
    - "bagpack a general framework to represent semantic relations"

Connected through hyperedge HE-4 (induced by source paper "cogalex v shared task root18"):
  with members:
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "computing lexical contrast"

Connected through hyperedge HE-5 (induced by source paper "counter fitting word vectors to linguistic constraints"):
  with members:
    - "non distributional word vector representations"
    - "computing lexical contrast"

Connected through hyperedge HE-6 (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"
    - "the latent relation mapping engine algorithm and experiments"
    - "expressing implicit semantic relations without supervision"

Connected through hyperedge HE-7 (induced by source paper "computing lexical contrast"):
  with members:
    - "analogy perception applied to seven tests of word comprehension"
    - "thumbs up sentiment classification using machine learning techniques"

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"
    - "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"
    - "combining independent modules to solve multiple choice synonym and analogy problems"

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)