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: "similarity of semantic relations"
  Abstract: There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.

Connected through hyperedge HE-1 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project"):
  with members:
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "repurposing entailment for multi hop question answering tasks"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-2 (induced by source paper "quantifying similarity between relations with fact distribution"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "unsupervised open relation extraction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "classifying relations by ranking with convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "on understanding knowledge graph representation"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "convolutional 2d knowledge graph embeddings"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "deconstructing word embeddings"):
  with members:
    - "evaluating vector space models of analogy"
    - "poincar e embeddings for learning hierarchical representations"
    - "issues in evaluating semantic spaces using word analogies"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "code2vec learning distributed representations of code"):
  with members:
    - "a general path based representation for predicting program properties"
    - "neural attribute machines for program generation"
    - "attention is all you need"
    - "zero shot relation extraction via reading comprehension"
    - "a survey of machine learning for big code and naturalness"
    - "neural sketch learning for conditional program generation"
    - "data driven program completion"
    - "learning to represent programs with graphs"

Connected through hyperedge HE-7 (induced by source paper "sentence entailment in compositional distributional semantics"):
  with members:
    - "a generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras"
    - "the frobenius anatomy of word meanings ii possessive relative pronouns"
    - "coordination in categorical compositional distributional semantics"
    - "graded entailment for compositional distributional semantics"
    - "a frobenius model of information structure in categorical compositional distributional semantics"
    - "open system categorical quantum semantics in natural language processing"
    - "distributional sentence entailment using density matrices"
    - "evaluating neural word representations in tensor based compositional settings"

Connected through hyperedge HE-8 (induced by source paper "semantic vector spaces for broadening consideration of consequences"):
  with members:
    - "concrete problems in ai safety"
    - "from a to z supervised transfer of style and content using deep neural network generators"
    - "skip thought vectors"
    - "modeling relation paths for representation learning of knowledge bases"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "retrofitting word vectors to semantic lexicons"

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)