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 "optimization vs reinforcement learning for wirelessly powered sensor networks"):
  Abstract: We consider a sensing application where the sensor nodes are wirelessly powered by an energy beacon. We focus on the problem of jointly optimizing the energy allocation of the energy beacon to different sensors and the data transmission powers of the sensors in order to minimize the field reconstruction error at the sink. In contrast to the standard ideal linear energy harvesting (EH) model, we consider practical non-linear EH models. We investigate this problem under two different frameworks: i) an optimization approach where the energy beacon knows the utility function of the nodes, channel state information and the energy harvesting characteristics of the devices; hence optimal power allocation strategies can be designed using an optimization problem and ii) a learning approach where the energy beacon decides on its strategies adaptively with battery level information and feedback on the utility function. Our results illustrate that deep reinforcement learning approach can obtain the same error levels with the optimization approach and provides a promising alternative to the optimization framework.

Includes members:
  Member "waveform design for wireless power transfer with limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "wireless power transfer for distributed estimation in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed sensing with orthogonal multiple access to code or not to code")
  Member "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "waveform design for wireless power transfer":
    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 "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff":
    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 "benchmarking deep reinforcement learning for continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "practical non linear energy harvesting model and resource allocation for swipt systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)