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: "a learning approach to wireless information and power transfer signal and system design"
  Abstract: The end-to-end learning of Simultaneous Wireless Information and Power Transfer (SWIPT) over a noisy channel is studied. Adopting a nonlinear model for the energy harvester (EH) at the receiver, a joint optimization of the transmitter and the receiver is implemented using Deep Neural Network (DNN)-based autoencoders. Modulation constellations for different levels of "power" and "information rate" demand at the receiver are obtained. The numerically optimized signal constellations are inline with the previous theoretical results. In particular, it is observed that as the receiver energy demand increases, all but one of the modulation symbols are concentrated around the origin and the other symbol is shot away from the origin along either the real or imaginary subchannel.

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"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"

Connected through hyperedge HE-2 (induced by source paper "specific absorption rate aware beamforming in miso downlink swipt systems"):
  with members:
    - "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"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"
    - "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model"
    - "waveform design for wireless power transfer"
    - "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff"
    - "safe and secure wireless power transfer networks challenges and opportunities in rf based systems"
    - "communications and signals design for wireless power transmission"

Connected through hyperedge HE-3 (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"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "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-4 (induced by source paper "rate power region of swipt systems employing nonlinear energy harvester circuits with memory"):
  with members:
    - "conditional capacity and transmit signal design for swipt systems with multiple nonlinear energy harvesting receivers"
    - "learning modulation design for swipt with nonlinear energy harvester large and small signal power regimes"
    - "wireless information and power transfer nonlinearity waveform design and rate energy tradeoff"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "model free training of end to end communication systems"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
    - "deep joint source channel coding for wireless image transmission"
    - "neural joint source channel coding"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"

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)