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: "analog spatial decoupling for tackling the near far problem in wirelessly powered communications"
  Abstract: A practical architecture for wirelessly powered communications (WPC) with dedicated power beacons (PBs) deployed in existing cellular networks, called PB-assisted WPC, is considered in this paper. Assuming those PBs can access to backhaul network and perform simultaneous wireless information and power transfer (SWIPT) to the energy constrained users, the near-far problem in the PB-assisted WPC system is first identified. Specifically, the significant difference of the received power of SWIPT signal (from a PB) and information transfer (IT) signal (from a base station) due to different transmission ranges leads to extremely small signal-to-quantization-noise ratio (SQNR) for the IT signal after quantization of the mixed signals. To retrieve the information carried by the SWIPT and IT signals respectively, it is essential to decouple the strong SWIPT and the weak IT signals in analog domain. To this end, a novel technique called analog spatial decoupling using only simple components such as phase shifters and adders is proposed in this paper. In particular, for the single-PB case, the optimal Fourier based and Hadamard based schemes are proposed for implementing the analog spatial decoupling. For the multiple-PB case, the corresponding design problem is more challenging, making it hard to extend the solution for the single-PB counterpart. To tackle this problem, a systematic solution approach is proposed for analog decoupling with multiple PBs.

Connected through hyperedge HE-1 (induced by source paper "some new research trends in wirelessly powered communications"):
  with members:
    - "wireless powered communication opportunities and challenges"
    - "multi antenna wireless energy transfer for backscatter communication systems"
    - "waveform optimization for wireless power transfer with nonlinear energy harvester modeling"
    - "cutting last wires for mobile communication by microwave power transfer"
    - "communication using a large scale array of ubiquitous antennas a geometry approach"
    - "enabling wireless power transfer in cellular networks architecture modeling and deployment"
    - "mimo broadcasting for simultaneous wireless information and power transfer"

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)