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: "cooperation optimized design for information dissemination in vehicular networks using evolutionary game theory"
  Abstract: We present an evolutionary game theoretic approach to study node cooperation behavior in wireless ad hoc networks. Evolutionary game theory (EGT) has been used to study the conditions governing the growth of cooperation behavior in biological and social networks. We propose a model of node cooperation behavior in dynamic wireless networks such as vehicular networks. Our work is motivated by the fact that, similar to existing EGT studies, node behavior in dynamic wireless networks is characterized by decision making that only depends on the immediate neighborhood. We adapt our model to study cooperation behavior in the context of information dissemination in wireless networks. We obtain conditions that determine whether a network evolves to a state of complete cooperation from all nodes. Finally, we use our model to study the evolution of cooperation behavior and its impact on content downloading in vehicular networks, taking into consideration realistic network conditions.

Connected through hyperedge HE-1 (induced by source paper "spreading processes in multilayer networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "multidimensional epidemic thresholds in diffusion processes over interdependent networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "measuring large scale social networks with high resolution"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "diffusion of innovations over multiplex social networks"
    - "exploiting cellular data for disease containment and information campaigns strategies in country wide epidemics"
    - "combinatorial analysis of multiple 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)