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: "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
  Abstract: The papers of Mikolov et al. 2013 as well as subsequent works have led to dramatic progress in solving word analogy tasks using semantic word embeddings. This leverages linear structure that is often found in the word embeddings, which is surprising since the training method is usually nonlinear. There were attempts ---notably by Levy and Goldberg and Pennington et al.--- to explain how this linear structure arises. The current paper points out the gaps in these explanations and provides a more complete explanation using a loglinear generative model for the corpus that directly models the latent semantic structure in words. The novel methodological twist is that instead of trying to fit the best model parameters to the data, a rigorous mathematical analysis is performed using the model priors to arrive at a simple closed form expression that approximately relates co-occurrence statistics and word embeddings. This expression closely corresponds to ---and a bit simpler than--- the existing training methods, and leads to good solutions to analogy tasks. Empirical support is provided also for the validity of the modeling assumptions. #R##N#This methodology of letting some mathematical analysis substitute for some of the computational difficulty may be useful in other settings with generative models.

Connected through hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "understanding the origins of bias in word embeddings"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "towards understanding linear word analogies"

Connected through hyperedge HE-2 (induced by source paper "the spectral underpinning of word2vec"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "multiview kernels for low dimensional modeling of seismic events"
    - "kernel scaling for manifold learning and classification"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "recovering structured probability matrices"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "learning mixtures of gaussians in high dimensions"
    - "testing closeness with unequal sized samples"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "efficient estimation of word representations in vector space"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-4 (induced by source paper "enabling cognitive intelligence queries in relational databases using low dimensional word embeddings"):
  with members:
    - "multilingual relation extraction using compositional universal schema"
    - "incremental knowledge base construction using deepdive"
    - "document embedding with paragraph vectors"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "word representations via gaussian embedding"

Connected through hyperedge HE-5 (induced by source paper "semantic regularities in document representations"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "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 "using a distributional semantic vector space with a knowledge base for reasoning in uncertain conditions"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"
    - "corpus based learning of analogies and semantic relations"

Connected through hyperedge HE-7 (induced by source paper "from word embeddings to item recommendation"):
  with members:
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (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"
    - "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"
    - "natural language processing almost from scratch"

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)