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: "measuring semantic similarity by latent relational analysis"
  Abstract: This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they are analogous. For example, the pair cat:meow is analogous to the pair dog:bark. There is evidence from cognitive science that relational similarity is fundamental to many cognitive and linguistic tasks (e.g., analogical reasoning). In the Vector Space Model (VSM) approach to measuring relational similarity, the similarity between two pairs is calculated by the cosine of the angle between the vectors that represent the two pairs. The elements in the vectors are based on the frequencies of manually constructed patterns in a large corpus. LRA extends the VSM approach in three ways: (1) patterns are derived automatically from the corpus, (2) Singular Value Decomposition is used to smooth the frequency data, and (3) synonyms are used to reformulate word pairs. This paper describes the LRA algorithm and experimentally compares LRA to VSM on two tasks, answering college-level multiple-choice word analogy questions and classifying semantic relations in noun-modifier expressions. LRA achieves state-of-the-art results, reaching human-level performance on the analogy questions and significantly exceeding VSM performance on both tasks.

Connected through hyperedge HE-1 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

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

Connected through hyperedge HE-4 (induced by source paper "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"):
  with members:
    - "corpus based learning of analogies and semantic relations"

Connected through hyperedge HE-5 (induced by source paper "pair2vec compositional word pair embeddings for cross sentence inference"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-6 (induced by source paper "seven augmenting word embeddings with unsupervised relation vectors"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "modeling semantic relatedness using global relation vectors"
    - "specialising word vectors for lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"

Connected through hyperedge HE-7 (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-8 (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"

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.LG(Machine Learning)