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 capacity achieving distributions over complex awgn channels under nonlinear power constraints and their applications to swipt"
  Abstract: The capacity of deterministic, complex and discrete time memoryless Additive White Gaussian Noise (AWGN) channel under three constraints, namely, channel input average power, channel input amplitude and delivered power at the channel output is considered. The delivered power constraint is modelled as a linear combination of even-moment statistics of the channel input being larger than a threshold. It is shown that the capacity of an AWGN channel under transmit average power and receiver delivered power constraints is the same as the capacity of an AWGN channel under an average power constraint, however, depending on the two constraints, it can be either achieved or arbitrarily approached. It is also shown that under average power, amplitude and delivered power constraints, the optimal capacity achieving distributions are discrete with a finite number of mass points. To establish the results, the confluent hypergeometric functions as well as the output rate of decay of complex Gaussian channels are utilized extensively. As an application, a simultaneous information and power transfer (SWIPT) problem is studied, where an experimentally-validated nonlinear model of the harvester is used. Relying on small signal analysis approximation, a general form of the delivered Direct-Current (DC) power in terms of system baseband parameters is derived for independent and identically distributed (iid) inputs. It is shown that the delivered power depends on higher order statistics of the channel input. By defining the rate-power (RP) region, two inner bounds, one based on complex Gaussian inputs and the other based on convexifying the optimization probability space, are obtained.

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 "asymmetric modulation design for wireless information and power transfer with nonlinear energy harvesting"):
  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"
    - "large scale multiantenna multisine wireless power transfer"
    - "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"
    - "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model"
    - "swipt with practical modulation and rf energy harvesting sensitivity"

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"
    - "a learning approach to wireless information and power transfer signal and system design"
    - "an introduction to deep learning for the physical layer"
    - "on the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"

Connected through hyperedge HE-4 (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 the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "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-5 (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 the capacity of swipt systems with a nonlinear energy harvesting circuit"
    - "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-6 (induced by source paper "signal and system design for wireless power transfer prototype experiment and validation"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "wireless energy beamforming using received signal strength indicator feedback"
    - "waveform design for wireless power transfer with limited feedback"
    - "large scale multiantenna multisine wireless power transfer"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on the convex properties of wireless power transfer with nonlinear energy harvesting"):
  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"
    - "heterogeneous power splitting based two way df relaying with non linear energy harvesting"
    - "on the beneficial roles of fading and transmit diversity in wireless power transfer with nonlinear energy harvesting"
    - "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"
    - "communications and signals design for wireless power transmission"

Connected through hyperedge HE-8 (induced by source paper "modulation design for wireless information and power transfer with nonlinear energy harvester modeling"):
  with members:
    - "fundamentals of wireless information and power transfer from rf energy harvester models to signal and system designs"
    - "large scale multiantenna multisine wireless power transfer"
    - "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"
    - "swipt with practical modulation and rf energy harvesting sensitivity"
    - "waveform design for wireless power transfer"

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)