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: "the spacey random walk a stochastic process for higher order data"
  Abstract: Random walks are a fundamental model in applied mathematics and are a common example of a Markov chain. The limiting stationary distribution of the Markov chain represents the fraction of the time spent in each state during the stochastic process. A standard way to compute this distribution for a random walk on a finite set of states is to compute the Perron vector of the associated transition matrix. There are algebraic analogues of this Perron vector in terms of transition probability tensors of higher-order Markov chains. These vectors are nonnegative, have dimension equal to the dimension of the state space, and sum to one, and they are derived by making an algebraic substitution in the equation for the joint-stationary distribution of a higher-order Markov chain. Here, we present the spacey random walk, a non-Markovian stochastic process whose stationary distribution is given by the tensor eigenvector. The process itself is a vertex-reinforced random walk, and its discrete dynamics are related to a c...

Connected through hyperedge HE-1 (induced by source paper "nonlocal pagerank"):
  with members:
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "three hypergraph eigenvector centralities"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "higher order organization of complex networks"
    - "localization and centrality in networks"
    - "total communicability as a centrality measure"

Connected through hyperedge HE-2 (induced by source paper "hetespaceywalk a heterogeneous spacey random walk for heterogeneous information network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "carl content aware representation learning for heterogeneous networks"
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-3 (induced by source paper "efficient representation learning using random walks for dynamic graphs"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "network representation learning a survey"
    - "representation learning on graphs methods and applications"
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "quadratic decomposable submodular function minimization"
    - "inhomogoenous hypergraph clustering with applications"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"
    - "motif clustering and overlapping clustering for social network analysis"

Connected through hyperedge HE-5 (induced by source paper "pairwise link prediction"):
  with members:
    - "the hyperkron graph model for higher order features"
    - "simplicial closure and higher order link prediction"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "higher order organization of complex networks"
    - "pagerank beyond the web"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-6 (induced by source paper "network embedding as matrix factorization unifying deepwalk line pte and node2vec"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "a latent variable model approach to pmi based word embeddings"
    - "multi modal bayesian embeddings for learning social knowledge graphs"
    - "comprehend deepwalk as matrix factorization"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-7 (induced by source paper "the hyperkron graph model for higher order features"):
  with members:
    - "higher order clustering in networks"
    - "higher order organization of complex networks"
    - "a scalable generative graph model with community structure"
    - "an in depth analysis of stochastic kronecker graphs"
    - "social structure of facebook networks"

Connected through hyperedge HE-8 (induced by source paper "three hypergraph eigenvector centralities"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "general centrality in a hypergraph"

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.NA(Numerical Analysis)