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: "mapping higher order network flows in memory and multilayer networks with infomap"
  Abstract: Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and  ...

Connected through hyperedge HE-1 (induced by source paper "community detection in multiplex networks"):
  with members:
    - "overlapping community detection by local decentralised vertex centred process"
    - "node centric community detection in multilayer networks with layer coverage diversification bias"
    - "clustering attributed graphs models measures and methods"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a local perspective on community structure in multilayer networks"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-2 (induced by source paper "representing big data as networks new methods and insights"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "higher order organization of complex networks"
    - "can cascades be predicted"
    - "using triangles to improve community detection in directed networks"
    - "graph based anomaly detection and description a survey"
    - "it doesn t break just on twitter characterizing facebook content during real world events"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "the role of social networks in information diffusion"

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)