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 energy transfer to a pair of energy receivers using signal strength feedback"
  Abstract: This paper focuses on wireless energy transfer (WET) to a pair of low complex energy receivers (ER), by only utilizing received signal strength indicator (RSSI) values that are fed back from the ERs to the energy transmitter (ET). Selecting the beamformer that maximizes the total average energy transfer between the ET and the ERs, while satisfying a minimum harvested energy criterion at each ER, is studied. This is a nonconvex constrained optimization problem which is difficult to solve analytically. Also, any analytical solution to the problem should only consists of parameters that the ET knows, or the ET can estimate, as utilizing only RSSI feedback values for channel estimation prohibits estimating some channel parameters. Thus, the paper focuses on obtaining a suboptimal solution analytically. It is proven that if the channels between the ET and the ERs satisfy a certain sufficient condition, this solution is in fact optimal. Simulations show that the optimality gap is negligibly small as well. Insights into a system with more than two ERs are also presented. To this end, it is highlighted that if the number of ERs is large enough, it is possible to always find a pair of ERs satisfying the sufficient condition, and hence, a pairwise scheduling policy that does not violate optimality can be used for the WET.

Connected through hyperedge HE-1 (induced by source paper "opportunistic wireless energy beamforming using rssi feedback"):
  with members:
    - "wireless energy beamforming using received signal strength indicator feedback"
    - "over the air implementation of uplink noma"
    - "on the efficiency of far field wireless power transfer"
    - "optimized training for net energy maximization in multi antenna wireless energy transfer over frequency selective channel"
    - "energy beamforming with one bit feedback"
    - "throughput maximization in wireless powered communication networks"
    - "spatially selective artificial noise aided transmit optimization for miso multi eves secrecy rate maximization"
    - "a novel mode switching scheme utilizing random beamforming for opportunistic energy harvesting"

Connected through hyperedge HE-2 (induced by source paper "cluster based wireless energy transfer for low complex energy receivers"):
  with members:
    - "wireless energy beamforming using received signal strength indicator feedback"
    - "waveform design for wireless power transfer with limited feedback"
    - "over the air implementation of uplink noma"
    - "on the efficiency of far field wireless power transfer"
    - "optimized training for net energy maximization in multi antenna wireless energy transfer over frequency selective channel"
    - "energy beamforming with one bit feedback"
    - "throughput maximization in wireless powered communication networks"
    - "spatially selective artificial noise aided transmit optimization for miso multi eves secrecy rate maximization"

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)