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: "wireless information and power transfer design for energy cooperation distributed antenna systems"
  Abstract: Distributed antenna systems (DAS) have been widely implemented in state-of-the-art cellular communication systems to cover dead spots. Recent studies have also indicated that DAS have advantages in wireless energy transfer (WET). In this paper, we study simultaneous wireless information and power transfer (SWIPT) for a multiple-input single-output (MISO) DAS in the downlink which consists of arbitrarily distributed remote antenna units (RAUs). In order to save the energy cost, we adopt energy cooperation of energy harvesting (EH) and two-way energy flows to let the RAUs trade their harvested energy through the smart grid network. Under individual EH constraints, per-RAU power constraints and various smart grid considerations, we investigate a power management strategy that determines how to utilize the stochastically spatially distributed harvested energy at the RAUs and how to trade the energy with the smart grid simultaneously to supply maximum wireless information transfer (WIT) with a minimum WET constraint for a receiver adopting power splitting (PS). Our analysis shows that the optimal design can be achieved in two steps. The first step is to maximize a new objective that can simultaneously maximize both WET and WIT, considering both the smart grid profitable and smart grid neutral cases. For the grid-profitable case, we derive the optimal full power strategy and provide a closed-form result to see under what condition this strategy is used. On the other hand, for the grid-neutral case, we illustrate that the optimal power policy has a double-threshold structure and present an optimal allocation strategy. The second step is then to solve the whole problem by obtaining the splitting power ratio based on the minimum WET constraint. Simulation results are provided to evaluate the performance under various settings and characterize the double-threshold structure.

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.IT(Information Theory)