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: "communities and hierarchical structures in dynamic social networks analysis and visualization"
  Abstract: Detection of community structures in social networks has attracted lots of attention in the domain of sociology and behavioral sciences. Social networks also exhibit dynamic nature as these networks change continuously with the passage of time. Social networks might also present a hierarchical structure led by individuals that play important roles in a society such as Managers and Decision Makers. Detection and Visualization of these networks changing over time is a challenging problem where communities change as a function of events taking place in the society and the role people play in it. #R##N#In this paper we address these issues by presenting a system to analyze dynamic social networks. The proposed system is based on dynamic graph discretization and graph clustering. The system allows detection of major structural changes taking place in social communities over time and reveals hierarchies by identifying influential people in a social networks. We use two different data sets for the empirical evaluation and observe that our system helps to discover interesting facts about the social and hierarchical structures present in these social networks.

Connected through hyperedge HE-1 (induced by source paper "detecting topological changes in dynamic community networks"):
  with members:
    - "a survey on social media anomaly detection"
    - "the resistance perturbation distance a metric for the analysis of dynamic networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "anomaly detection in dynamic networks of varying size"
    - "exact recovery in the stochastic block model"
    - "stochastic block transition models for dynamic networks"
    - "intrinsically dynamic network communities"

Connected through hyperedge HE-2 (induced by source paper "network generation model based on evolution dynamics to generate benchmark graphs"):
  with members:
    - "social structure of facebook networks"
    - "a large scale community structure analysis in facebook"
    - "defining and evaluating network communities based on ground truth"
    - "qualitative comparison of community detection algorithms"
    - "empirical comparison of algorithms for network community detection"
    - "a unified approach to mapping and clustering of bibliometric networks"

Connected through hyperedge HE-3 (induced by source paper "analysis and visualization of dynamic networks"):
  with members:
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "forensic analysis of phone call networks"):
  with members:
    - "a novel measure of edge centrality in social networks"
    - "a classification for community discovery methods in complex networks"
    - "generalized louvain method for community detection in large networks"
    - "topological features of online social networks"

Connected through hyperedge HE-5 (induced by source paper "development of computer science disciplines a social network analysis approach"):
  with members:
    - "citing for high impact"
    - "a principal component analysis of 39 scientific impact measures"

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)