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 "information and energy transmission with experimentally sampled harvesting functions"):
  Abstract: This paper considers the problem of simultaneous information and energy transmission (SIET), where the energy harvesting function is only known experimentally at sample points, e.g., due to nonlinearities and parameter uncertainties in harvesting circuits. We investigate the performance loss due to this partial knowledge of the harvesting function in terms of transmitted energy and information. In particular, we assume harvesting functions are a subclass of Sobolev space and consider two cases, where experimental samples are either taken noiselessly or in the presence of noise. Using constructive function approximation and regression methods for noiseless and noisy samples respectively, we show that the worst loss in energy transmission vanishes asymptotically as the number of samples increases. Similarly, the worst loss in information rate vanishes in the interior of the energy domain, however, does not always vanish at maximal energy. We further show the same principle applies in multicast settings such as medium access in the Wi-Fi protocol. We also consider the end-to-end source-channel communication problem under source distortion constraint and channel energy requirement, where distortion and harvesting functions both are known only at samples.

Includes members:
  Member "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "design of an miso swipt aided code index modulated multi carrier m dcsk system for e health iot")
  Member "multicasting energy and information simultaneously":
    also connected through other hyperedges:
      (none)
  Member "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")
  Member "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff":
    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 "practical non linear energy harvesting model and resource allocation for swipt systems":
    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 "feedback enhances simultaneous wireless information and energy transmission in multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "specific absorption rate aware beamforming in miso downlink swipt systems")
  Member "wireless information and power transfer architecture design and rate energy tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cognitive wireless power transfer in the presence of reactive primary communication user")
  Member "on the transfer of information and energy in multi user systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy sustainable paradigms and methods for future mobile networks 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)