human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "low complexity adaptive multisine waveform design for wireless power transfer"):
  Abstract: Channel-adaptive waveforms for wireless power transfer significantly boost the dc power level at the rectifier output. However, the design of those waveforms is computationally complex and does not lend itself easily to practical implementation. We here propose a low-complexity channel-adaptive waveform design whose performance is very close to that of the optimal design. Performance evaluations confirm the advantages of the new design in various rectifier topologies, with gains in dc output power of 100% over conventional waveforms.

Includes members:
  Member "prototyping and experimentation of a closed loop wireless power transmission with channel acquisition and waveform optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymmetric modulation design for wireless information and power transfer with nonlinear energy harvesting")
  Member "waveform design for wireless power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "waveform optimization for large scale multi antenna multi sine wireless power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "waveform design for wireless power transfer with limited feedback")
  Member "communications and signals design for wireless power transmission":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "waveform optimization for wireless power transfer with nonlinear energy harvester modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimization vs reinforcement learning for wirelessly powered sensor networks")

Question: Please tell me which class the source paper 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)