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: "fast change point detection on dynamic social networks"
  Abstract: A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.

Connected through hyperedge HE-1 (induced by source paper "change detection in noisy dynamic networks a spectral embedding approach"):
  with members:
    - "change detection in dynamic attributed networks"
    - "a probabilistic framework to node level anomaly detection in communication networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "link prediction for interdisciplinary collaboration via co authorship network"
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "detection and localization of change points in temporal networks with the aid of stochastic block models"
    - "localization and centrality in networks"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-2 (induced by source paper "detecting stable communities in link streams at multiple temporal scales"):
  with members:
    - "detecting sequences of system states in temporal networks"
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "community discovery in dynamic networks a survey"
    - "motifs in temporal networks"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "detecting change points in the large scale structure of evolving networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "detecting sequences of system states in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "concept drift and anomaly detection in graph streams"
    - "measuring large scale social networks with high resolution"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "modeling complex systems with adaptive networks"

Connected through hyperedge HE-4 (induced by source paper "size agnostic change point detection framework for evolving networks"):
  with members:
    - "motifs in temporal networks"
    - "random graph models for dynamic networks"
    - "social networks under stress"
    - "detecting change points in the large scale structure of evolving networks"
    - "multi level anomaly detection on time varying graph data"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-5 (induced by source paper "hierarchical change point detection on dynamic networks"):
  with members:
    - "graphprints towards a graph analytic method for network anomaly detection"
    - "anomaly detection in dynamic networks of varying size"
    - "detecting change points in the large scale structure of evolving networks"
    - "multi level anomaly detection on time varying graph data"
    - "graph based anomaly detection and description a survey"
    - "a theoretical analysis of ndcg type ranking measures"
    - "community structure and scale free collections of erdős renyi graphs"

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)