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: "community detection via local dynamic interaction"
  Abstract: How can we uncover the natural communities in a real network that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots the communities or groups in a network over time via local dynamic interaction. The basic idea is to envision a network as a dynamical system, and each agent interacts with its local partners. Instead of investigating the node dynamics, we actually examine the change of "distances" among linked nodes. As time evolves, these distances will be shrunk or stretched gradually based on their topological structures. Finally all distances among linked nodes will converge into a stable pattern, and communities can be intuitively identified. Thanks to the dynamic viewpoint of community detection, Attractor has several potential attractive properties: (a) Attractor provides an intuitive solution to analyze the community structure of a network, and faithfully captures the natural communities (with high quality). (b) Owing to its time complexity $O(|E|)$, Attractor allows finding communities on large networks. (c) The small communities or anomalies, usually existing in real-world networks, can be well pinpointed. (d) Attractor is easy to parameterize, since there is no need to specify the number of clusters. Extensive experiments on synthetic and real-world networks further demonstrate the effectiveness and efficiency of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

Connected through hyperedge HE-2 (induced by source paper "high quality disjoint and overlapping community structure in large scale complex networks"):
  with members:
    - "dynamic structural similarity on graphs"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "improving pagerank for local community detection"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "heat kernel based community detection"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-3 (induced by source paper "dynamic structural similarity on graphs"):
  with members:
    - "detecting global bridges in networks"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "heat kernel based community detection"

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)