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 "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.

Includes members:
  Member "gaussian multiple access channels with one bit quantizer at the receiver":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on multiterminal communication over mimo channels with one bit adcs at the receivers")
  Member "modulation design for wireless information and power transfer with nonlinear energy harvester modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "design of an miso swipt aided code index modulated multi carrier m dcsk system for e health iot")
  Member "on the capacity of swipt systems with a nonlinear energy harvesting circuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")
  Member "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")
  Member "prototyping and experimentation of a closed loop wireless power transmission with channel acquisition and waveform optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymmetric modulation design for wireless information and power transfer with nonlinear energy harvesting")
  Member "low complexity adaptive multisine waveform design for wireless power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to communicate and energize modulation coding and multiple access designs for wireless information power transmission")
  Member "waveform design for wireless power transfer":
    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 "waveform optimization for swipt with nonlinear energy harvester modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi tone signal optimization for wireless power transfer in the presence of wireless communication links")

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)