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 "waveform design for wireless power transfer"):
  Abstract: Far-field Wireless Power Transfer (WPT) has attracted significant attention in recent years. Despite the rapid progress, the emphasis of the research community in the last decade has remained largely concentrated on improving the design of energy harvester (so-called rectenna) and has left aside the effect of transmitter design. In this paper, we study the design of transmit waveform so as to enhance the dc power at the output of the rectenna. We derive a tractable model of the nonlinearity of the rectenna and compare with a linear model conventionally used in the literature. We then use those models to design novel multisine waveforms that are adaptive to the channel state information (CSI). Interestingly, while the linear model favours narrowband transmission with all the power allocated to a single frequency, the nonlinear model favours a power allocation over multiple frequencies. Through realistic simulations, waveforms designed based on the nonlinear model are shown to provide significant gains (in terms of harvested dc power) over those designed based on the linear model and over nonadaptive waveforms. We also compute analytically the theoretical scaling laws of the harvested energy for various waveforms as a function of the number of sinewaves and transmit antennas. Those scaling laws highlight the benefits of CSI knowledge at the transmitter in WPT and of a WPT design based on a nonlinear rectenna model over a linear model. Results also motivate the study of a promising architecture relying on large-scale multisine multiantenna waveforms for WPT. As a final note, results stress the importance of modeling and accounting for the nonlinearity of the rectenna in any system design involving wireless power.

Includes members:
  Member "waveform optimization for swipt with nonlinear energy harvester modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi tone signal optimization for wireless power transfer in the presence of wireless communication links")
  Member "waveform optimization for large scale multi antenna multi sine wireless power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "waveform design for wireless power transfer with limited feedback")
  Member "simultaneous wireless information and power transfer in a two user ofdm interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi tone signal optimization for wireless power transfer in the presence of wireless communication links")
  Member "waveform optimization for wireless power transfer with nonlinear energy harvester modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimization vs reinforcement learning for wirelessly powered sensor networks")
  Member "energy beamforming with one bit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "simultaneous information and power transfer for broadband wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transmission delay minimization in wireless powered communication systems")
  Member "wireless information and power transfer architecture design and rate energy tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cognitive wireless power transfer in the presence of reactive primary communication user")

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)