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 "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"):
  Abstract: Nonlinear energy harvesters (EH) behave differently depending on the range of their input power. In the literature, different models have been proposed mainly for relatively small and large input power regimes of an EH. Due to the complexity of the proposed nonlinear models, obtaining analytical optimal or well performing signal designs have been extremely challenging. Relying on the proposed models in the literature, the learning problem of modulation design for simultaneous wireless information-power transfer (SWIPT) over a point-to-point link is studied. Joint optimization of the transmitter and the receiver is implemented using neural network (NN)-based autoencoders. The results reveal that for relatively small channel input powers, as the power demand increases at the receiver, one of the symbols is shot away from the origin while the remaining symbols approach zero amplitude. In the very extreme case of merely receiver power demand, the modulations are in the form of On-Off keying signalling with a low probability of the On signal. On the other side, for relatively large channel input powers, it is observed that as the receiver power demand increases, a number of symbols approach zero amplitude, whereas the others (more than one symbol) get equally high amplitudes but with different phases. In the extreme scenario of merely receiver power demand, the modulation resembles multiple On-Off keying signalling with different phases.

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 "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "ofdm autoencoder for end to end learning of communications systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "model free training of end to end communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distortion agnostic deep watermarking")
  Member "a learning approach to wireless information and power transfer signal and system design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "on the capacity of swipt systems with a nonlinear energy harvesting circuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")
  Member "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")

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)