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: "anomaly detection in dynamic networks of varying size"
  Abstract: Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, social networks, or internet traffic networks are best represented by a dynamic network due to the temporal component of the data. One important application in the domain of dynamic network analysis is anomaly detection. Here the task is to identify points in time where the network exhibits behavior radically different from a typical time, either due to some event (like the failure of machines in a computer network) or a shift in the network properties. This problem is made more difficult by the fluid nature of what is considered "normal" network behavior. The volume of traffic on a network, for example, can change over the course of a month or even vary based on the time of the day without being considered unusual. Anomaly detection tests using traditional network statistics have difficulty in these scenarios due to their Density Dependence: as the volume of edges changes the value of the statistics changes as well making it difficult to determine if the change in signal is due to the traffic volume or due to some fundamental shift in the behavior of the network. To more accurately detect anomalies in dynamic networks, we introduce the concept of Density-Consistent network statistics. On synthetically generated graphs anomaly detectors using these statistics show a a 20-400% improvement in the recall when distinguishing graphs drawn from different distributions. When applied to several real datasets Density-Consistent statistics recover multiple network events which standard statistics failed to find.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "communities and hierarchical structures in dynamic social networks analysis and visualization"
    - "exact recovery in the stochastic block model"
    - "stochastic block transition models for dynamic networks"
    - "intrinsically dynamic network communities"

Connected through hyperedge HE-3 (induced by source paper "the resistance perturbation distance a metric for the analysis of dynamic networks"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "fast parallel graphlet counting for large networks"
    - "graphlet decomposition framework algorithms and applications"
    - "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"
    - "practical graph isomorphism ii"
    - "spectral sparsification of 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)