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: "multi level anomaly detection on time varying graph data"
  Abstract: This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. #R##N#For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

Connected through hyperedge HE-1 (induced by source paper "quantifiable comparable evaluations of cyber defensive capabilities a survey novel unified approach"):
  with members:
    - "how do information security workers use host data a summary of interviews with security analysts"
    - "graphprints towards a graph analytic method for network anomaly detection"
    - "coordination in network security games a monotone comparative statics approach"

Connected through hyperedge HE-2 (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"
    - "fast change point detection on dynamic social networks"
    - "social networks under stress"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-3 (induced by source paper "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "a scalable generative graph model with community structure"
    - "triadic measures on graphs the power of wedge sampling"
    - "community structure and scale free collections of erdős renyi graphs"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-4 (induced by source paper "hierarchical change point detection on dynamic networks"):
  with members:
    - "fast change point detection on dynamic social networks"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "fast change point detection on dynamic social networks"):
  with members:
    - "random graph models for dynamic networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "detecting weak changes in dynamic events over networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "fast generation of large scale social networks with clustering"
    - "community structure and scale free collections of erdős renyi graphs"
    - "netsimile a scalable approach to size independent network similarity"

Connected through hyperedge HE-6 (induced by source paper "graphprints towards a graph analytic method for network anomaly detection"):
  with members:
    - "towards a network of networks framework for cyber security"

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)