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: "general tensor spectral co clustering for higher order data"
  Abstract: Spectral clustering and co-clustering are well-known techniques in data analysis, and recent work has extended spectral clustering to square, symmetric tensors and hypermatrices derived from a network. We develop a new tensor spectral co-clustering method that applies to any non-negative tensor of data. The result of applying our method is a simultaneous clustering of the rows, columns, and slices of a three-mode tensor, and the idea generalizes to any number of modes. The algorithm we design works by recursively bisecting the tensor into two pieces. We also design a new measure to understand the role of each cluster in the tensor. Our new algorithm and pipeline are demonstrated in both synthetic and real-world problems. On synthetic problems with a planted higher-order cluster structure, our method is the only one that can reliably identify the planted structure in all cases. On tensors based on n-gram text data, we identify stop-words and semantically independent sets; on tensors from an airline-airport multimodal network, we find worldwide and regional co-clusters of airlines and airports; and on tensors from an email network, we identify daily-spam and focused-topic sets.

Connected through hyperedge HE-1 (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"
    - "the spacey random walk a stochastic process for higher order data"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "spectral clustering methods for multiplex networks"):
  with members:
    - "geometric multiscale community detection markov stability and vector partitioning"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "detectability of small communities in multilayer and temporal networks eigenvector localization layer aggregation and time series discretization"
    - "non negative matrix factorizations for multiplex network analysis"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "a new framework for dynamical models on multiplex networks"
    - "network models that reflect multiplex dynamics"
    - "a local perspective on community structure in multilayer networks"

Connected through hyperedge HE-3 (induced by source paper "retrospective higher order markov processes for user trails"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "linear additive markov processes"
    - "higher order organization of complex networks"
    - "the new data and new challenges in multimedia research"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "tribeflow mining predicting user trajectories"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-4 (induced by source paper "higher order minimum cost lifted multicuts for motion segmentation"):
  with members:
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "learning to segment moving objects in videos"
    - "opengm a c library for discrete graphical models"

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.SI(Social and Information Networks)