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: "compressed control of complex wireless networks"
  Abstract: Future wireless networks are envisioned to integrate multi-hop multi-operator multi-technology (   $\text{m}^{3}$   ) components in order to meet the increasing traffic demand at an acceptable price for subscribers. The performance of such a network depends on the multitude of parameters defining traffic statistics, network topology/technology, channel characteristics, and business models for multi-operator cooperation. So far, most of these aspects have been separately addressed in the literature. Since the above parameters are mutually dependent and simultaneously present in a network, for a given channel and traffic statistics, a joint optimization of technology and business model parameters is required. In this paper, we present such joint models of complex wireless networks and introduce optimization with parameter clustering to solve the problem in a tractable way for large number of parameters. By parameter clustering, we compress the optimization vector and significantly simplify system implementation, and hence, the algorithm is referred to as the compressed control of wireless networks. Two distinct parameter compression techniques are introduced, namely, parameter absorption and parameter aggregation. Numerical results obtained in this way demonstrate clear maximum in the network utility as a function of the network topology parameters. The results, for a specific network with traffic offloading, show that the cooperation decisions between the multiple operators will be significantly influenced by the traffic dynamics. For typical example scenarios, the optimum offloading price varies by factor 3 for different traffic patterns, which justifies the use of dynamic strategies in the decision process. Besides, if user availability increases by multi-operator cooperation, network capacity can be increased up to 50% and network throughput up to 30%–40%.

Connected through hyperedge HE-1 (induced by source paper "data and spectrum trading policies in a trusted cognitive dynamic network"):
  with members:
    - "joint resource bidding and tipping strategies in multi hop cognitive networks"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"
    - "flexauc serving dynamic demands in a spectrum trading market with flexible auction"

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)