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: "ranking and clustering of nodes in networks with smart teleportation"
  Abstract: Random teleportation is a necessary evil for ranking and clustering directed networks based on random walks. Teleportation enables ergodic solutions, but the solutions must necessarily depend on the exact implementation and parametrization of the teleportation. For example, in the commonly used PageRank algorithm, the teleportation rate must trade off a heavily biased solution with a uniform solution. Here we show that teleportation to links rather than nodes enables a much smoother trade-off and effectively more robust results. We also show that, by not recording the teleportation steps of the random walker, we can further reduce the effect of teleportation with dramatic effects on clustering.

Connected through hyperedge HE-1 (induced by source paper "multiscale dynamical embeddings of complex networks"):
  with members:
    - "the many facets of community detection in complex networks"
    - "node2vec scalable feature learning for networks"
    - "structure and inference in annotated networks"
    - "deepwalk online learning of social representations"
    - "a generalized and adaptive method for community detection"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "mapping higher order network flows in memory and multilayer networks with infomap"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "maps of sparse markov chains efficiently reveal community structure in network flows with memory"
    - "representing higher order dependencies in networks"
    - "multiaspect graphs algebraic representation and algorithms"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-3 (induced by source paper "viziometrics analyzing visual information in the scientific literature"):
  with members:
    - "automatic separation of compound figures in scientific articles"
    - "static ranking of scholarly papers using article level eigenfactor alef"

Connected through hyperedge HE-4 (induced by source paper "modularity in complex multilayer networks with multiple aspects a static perspective"):
  with members:
    - "combinatorial analysis of multiple networks"

Connected through hyperedge HE-5 (induced by source paper "a local perspective on community structure in multilayer networks"):
  with members:
    - "block models and personalized pagerank"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "spreading processes in multilayer networks"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "seeded pagerank solution paths"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "pagerank beyond the web"
    - "defining and evaluating network communities based on ground truth"

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)