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: "waveform and transceiver design for simultaneous wireless information and power transfer"
  Abstract: Simultaneous Wireless Information and Power Transfer (SWIPT) has attracted significant attention in the communication community. The problem of waveform design has however never been addressed so far. In this paper, we first investigate how a communication waveform (OFDM) and a power waveform (multisine) compare with each other in terms of harvested energy. We show that due to the non-linearity of the rectifier and the randomness of the information symbols, the OFDM waveform is less efficient than the multisine waveform for wireless power transfer. This observation motivates the design of a novel SWIPT transceiver architecture relying on the superposition of multisine and OFDM waveforms at the transmitter and a power-splitter receiver equipped with an energy harvester and an information decoder. The superposed SWIPT waveform is optimized so as to maximize the rate-energy region of the whole system. Its design is adaptive to the channel state information and result from a posynomial maximization problem that originates from the non-linearity of the energy harvester. Numerical results illustrate the performance of the derived waveforms and SWIPT architecture. Key (and refreshing) observations are that 1) a power waveform (superposed to a communication waveform) is useful to enlarge the rate-energy region of SWIPT, 2) a combination of power splitting and time sharing is in general the best strategy, 3) exploiting the nonlinearity of the rectifier is essential to design efficient SWIPT architecture, 4) a non-zero mean Gaussian input distribution outperforms the conventional capacity-achieving zero-mean Gaussian input distribution.

Connected through hyperedge HE-1 (induced by source paper "wireless information and power transfer over an awgn channel nonlinearity and asymmetric gaussian signaling"):
  with members:
    - "waveform design for wireless power transfer"
    - "waveform optimization for swipt with nonlinear energy harvester modeling"
    - "relaying strategies for wireless powered mimo relay networks"
    - "relaying protocols for wireless energy harvesting and information processing"
    - "mimo broadcasting for simultaneous wireless information and 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)