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: "worm epidemics in wireless ad hoc networks"
  Abstract: A dramatic increase in the number of computing devices with wireless communication capability has resulted in the emergence of a new class of computer worms which specifically target such devices. The most striking feature of these worms is that they do not require Internet connectivity for their propagation but can spread directly from device to device using a short-range radio communication technology, such as WiFi or Bluetooth. In this paper, we develop a new model for epidemic spreading of these worms and investigate their spreading in wireless ad hoc networks via extensive Monte Carlo simulations. Our studies show that the threshold behaviour and dynamics of worm epidemics in these networks are greatly affected by a combination of spatial and temporal correlations which characterize these networks, and are significantly different from the previously studied epidemics in the Internet.

Connected through hyperedge HE-1 (induced by source paper "local cliques in er perturbed random geometric graphs"):
  with members:
    - "a quest to unravel the metric structure behind perturbed networks"

Connected through hyperedge HE-2 (induced by source paper "characterization and inference of graph diffusion processes from observations of stationary signals"):
  with members:
    - "graph learning under sparsity priors"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "network scale traffic modeling and forecasting with graphical lasso and neural networks"
    - "graph reconstruction from the observation of diffused signals"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "network topology identification from spectral templates"

Connected through hyperedge HE-3 (induced by source paper "characterization and inference of weighted graph topologies from observations of diffused signals"):
  with members:
    - "graph learning under sparsity priors"
    - "stationary signal processing on graphs"
    - "network scale traffic modeling and forecasting with graphical lasso and neural networks"
    - "graph reconstruction from the observation of diffused signals"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "learning sparse graphs under smoothness prior"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-4 (induced by source paper "the game theoretic formation of interconnections between networks"):
  with members:
    - "the strategic formation of multi layer networks"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "network formation games among relay stations in next generation wireless networks"

Connected through hyperedge HE-5 (induced by source paper "the opportunistic transmission of wireless worms between mobile devices"):
  with members:
    - "wifi networks and malware epidemiology"

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)