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: "identifying roles in an ip network with temporal and structural density"
  Abstract: Captures of IP traffic contain much information on very different kinds of activities like file transfers, users interacting with remote systems, automatic backups, or distributed computations. Identifying such activities is crucial for an appropriate analysis, modeling and monitoring of the traffic. We propose here a notion of density that captures both temporal and structural features of interactions, and generalizes the classical notion of clustering coefficient. We use it to point out important differences between distinct parts of the traffic, and to identify interesting nodes and groups of nodes in terms of roles in the network.

Connected through hyperedge HE-1 (induced by source paper "predicting interactions between individuals with structural and dynamical information"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "predicting links in ego networks using temporal information"
    - "link prediction in social networks the state of the art"
    - "rankmerging a supervised learning to rank framework to predict links in large social network"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-2 (induced by source paper "non altering time scales for aggregation of dynamic networks into series of graphs"):
  with members:
    - "handling oversampling in dynamic networks using link prediction"
    - "detecting change points in the large scale structure of evolving networks"
    - "influence of the dynamic social network timeframe type and size on the group evolution discovery"
    - "centrality metric for dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "stream graphs and link streams for the modeling of interactions over time"):
  with members:
    - "degree based outlier detection within ip traffic modelled as a link stream"
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "movie rating prediction using content based and link stream features"
    - "motifs in temporal networks"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "time series analysis of temporal networks"
    - "graphprints towards a graph analytic method for network anomaly detection"
    - "semirings for temporal network analysis"

Connected through hyperedge HE-4 (induced by source paper "computing maximal cliques in link streams"):
  with members:
    - "a unifying model for representing time varying graphs"
    - "time varying graphs and dynamic networks"

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.NI(Networking and Internet Architecture)