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: "detecting overlapping temporal community structure in time evolving networks"
  Abstract: We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community structure that maximizes a quality function associated with each snapshot of the network subject to a temporal smoothness constraint. A novel quality function and a smoothness constraint are proposed to handle overlaps, and a new convex relaxation is used to solve the resulting combinatorial optimization problem. We provide theoretical guarantees as well as experimental results that reveal community structure in real and synthetic networks. Our main insight is that certain structures can be identified only when temporal correlation is considered and when communities are allowed to overlap. In general, discovering such overlapping temporal community structure can enhance our understanding of real-world complex networks by revealing the underlying stability behind their seemingly chaotic evolution.

Connected through hyperedge HE-1 (induced by source paper "temporally evolving community detection and prediction in content centric networks"):
  with members:
    - "predicting community evolution in social networks"
    - "predicting small group accretion in social networks a topology based incremental approach"
    - "different approaches to community evolution prediction in blogosphere"
    - "sequential detection of temporal communities by estrangement confinement"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-2 (induced by source paper "temporally evolving community detection and prediction in content centric networks"):
  with members:
    - "predicting community evolution in social networks"
    - "predicting small group accretion in social networks a topology based incremental approach"
    - "different approaches to community evolution prediction in blogosphere"
    - "sequential detection of temporal communities by estrangement confinement"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-3 (induced by source paper "duality between temporal networks and signals extraction of the temporal network structures"):
  with members:
    - "discovering the structure of complex networks by minimizing cyclic bandwidth sum"
    - "a regularized graph layout framework for dynamic network visualization"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "information spreading in stationary markovian evolving graphs"
    - "time varying graphs and dynamic networks"

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)