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 "energy beamforming with one bit feedback"):
  Abstract: Multi-antenna or multiple-input multiple-output (MIMO) techniques are appealing to enhance the transmission efficiency and range for radio frequency (RF) signal enabled wireless energy transfer (WET). In order to reap the energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. Specifically, each feedback bit indicates the increase or decrease of the harvested energy by each ER in the present as compared to the previous intervals, which can be measured without changing the existing structure of the ER. Based on such feedback information, the ET adjusts transmit beamforming in subsequent training intervals and at the same time obtains improved estimates of the MIMO channels to different ERs by applying an optimization technique called analytic center cutting plane method (ACCPM). For the proposed ACCPM based channel learning algorithm, we analyze its worst-case convergence, from which it is revealed that the algorithm is able to estimate multiuser MIMO channels simultaneously without reducing the analytic convergence speed. Also, we provide extensive simulations to show its performances in terms of both convergence speed and energy transfer efficiency.

Includes members:
  Member "multiuser miso beamforming for simultaneous wireless information and power transfer":
    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 "the one bit null space learning algorithm and its convergence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed wireless power transfer with energy feedback")
  Member "throughput maximization in wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on maximizing information reliability in wireless powered cooperative networks")
  Member "opportunistic wireless energy harvesting in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "design of an miso swipt aided code index modulated multi carrier m dcsk system for e health iot")
  Member "simultaneous information and power transfer for broadband wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transmission delay minimization in wireless powered communication systems")
  Member "relaying protocols for wireless energy harvesting and information processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on maximizing information reliability in wireless powered cooperative networks")
  Member "multi antenna wireless powered communication with energy beamforming":
    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 "wireless information transfer with opportunistic energy harvesting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "design of an miso swipt aided code index modulated multi carrier m dcsk system for e health iot")

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)