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: "wireless power transfer for distributed estimation in sensor networks"
  Abstract: This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors’ observation is locally processed by using an  uncoded amplify-and-forward  scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the  amplification coefficients  ,  energy beamforming , and  receive filtering . For each problem, a suboptimal design, a single-antenna FC scenario, and a  common harvester  deployment for collocated sensors, are also studied. Using the powerful  semidefinite relaxation  framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.

Connected through hyperedge HE-1 (induced by source paper "distributed sensing with orthogonal multiple access to code or not to code"):
  with members:
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "optimized sensor collaboration for estimation of temporally correlated parameters"
    - "delay limited source and channel coding of quasi stationary sources over block fading channels design and scaling laws"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-2 (induced by source paper "optimization vs reinforcement learning for wirelessly powered sensor networks"):
  with members:
    - "waveform design for wireless power transfer with limited feedback"
    - "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"
    - "benchmarking deep reinforcement learning for continuous control"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "trust region policy optimization"
    - "waveform optimization for wireless power transfer with nonlinear energy harvester modeling"

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)