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: "maps of sparse markov chains efficiently reveal community structure in network flows with memory"
  Abstract: To better understand the flows of ideas or information through social and biological systems, researchers develop maps that reveal important patterns in network flows. In practice, network flow mod ...

Connected through hyperedge HE-1 (induced by source paper "the many facets of community detection in complex networks"):
  with members:
    - "modelling sequences and temporal networks with dynamic community structures"
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "a new metric for quality of network community structure"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-2 (induced by source paper "modelling sequences and temporal networks with dynamic community structures"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "representing higher order dependencies in networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "model selection for degree corrected block models"
    - "detecting change points in the large scale structure of evolving networks"
    - "stochastic block transition models for dynamic networks"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "ranking and clustering of nodes in networks with smart teleportation"

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)