human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the capacity of swipt systems with a nonlinear energy harvesting circuit"
  Abstract: In this paper, we study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing a practical nonlinear radio frequency (RF) energy harvesting (EH) receiver. In particular, we consider a three-node system with one transmitter that broadcasts a common signal to separated information decoding (ID) and EH receivers. Owing to the nonlinearity of the EH receiver circuit, the efficiency of wireless power transfer depends significantly on the waveform of the transmitted signal. In this paper, we aim to answer the following fundamental question: What is the optimal input distribution of the transmit waveform that maximizes the rate of the ID receiver for a given required harvested power at the EH receiver? In particular, we study the capacity of a SWIPT system impaired by additive white Gaussian noise (AWGN) under average-power (AP) and peak-power (PP) constraints at the transmitter and an EH constraint at the EH receiver. Using Hermite polynomial bases, we prove that the optimal capacity-achieving input distribution that maximizes the rate-energy region is unique and discrete with a finite number of mass points. Furthermore, we show that the optimal input distribution for the same problem without PP constraint is discrete whenever the EH constraint is active and continuous zero-mean Gaussian, otherwise. Our numerical results show that the rate-energy region is enlarged for a larger PP constraint and that the rate loss of the considered SWIPT system compared to the AWGN channel without EH receiver is reduced by increasing the AP budget.

Connected through hyperedge HE-1 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission"):
  with members:
    - "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"
    - "signal and system design for wireless power transfer prototype experiment and validation"
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-2 (induced by source paper "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "model free training of end to end communication systems"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"

Connected through hyperedge HE-3 (induced by source paper "swipt signalling over frequency selective channels with a nonlinear energy harvester non zero mean and asymmetric inputs"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "sensitive and nonlinear far field rf energy harvesting in wireless communications"
    - "modulation design for wireless information and power transfer with nonlinear energy harvester modeling"
    - "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"
    - "prototyping and experimentation of a closed loop wireless power transmission with channel acquisition and waveform optimization"
    - "low complexity adaptive multisine waveform design for wireless power transfer"
    - "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model"

Connected through hyperedge HE-4 (induced by source paper "conditional capacity and transmit signal design for swipt systems with multiple nonlinear energy harvesting receivers"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"
    - "waveform optimization for radio frequency wireless power transfer"
    - "waveform design for wireless power transfer"
    - "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "capacity of the gaussian two hop full duplex relay channel with residual self interference"

Connected through hyperedge HE-5 (induced by source paper "a learning approach to wireless information and power transfer signal and system design"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "an introduction to deep learning for the physical layer"
    - "deep learning based mimo communications"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "low complexity adaptive multisine waveform design for wireless power transfer"

Connected through hyperedge HE-6 (induced by source paper "on capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"):
  with members:
    - "gaussian multiple access channels with one bit quantizer at the receiver"
    - "modulation design for wireless information and power transfer with nonlinear energy harvester modeling"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"
    - "prototyping and experimentation of a closed loop wireless power transmission with channel acquisition and waveform optimization"
    - "low complexity adaptive multisine waveform design for wireless power transfer"
    - "waveform design for wireless power transfer"
    - "waveform optimization for swipt with nonlinear energy harvester modeling"
    - "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff"

Question: Please tell me which class the center node 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)