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: "adaptively directional wireless power transfer for large scale sensor networks"
  Abstract: Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs’ locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the expressions of the distribution metrics of the aggregate received power at a typical SN. To design the charging radius for the optimal AD-WPT operation, we exploit the tradeoff between the power intensity of the energy beams and the number of SNs to be charged. Depending on different SN task requirements, the optimal AD-WPT can maximize the average received power or the active probability of the SNs, respectively. It is shown that both the maximum average received power and the maximum sensor active probability increase with the increased deployment density and transmit power of the PBs, and decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared with the traditional omnidirectional WPT.

Connected through hyperedge HE-1 (induced by source paper "big data analytics for large scale wireless networks challenges and opportunities"):
  with members:
    - "blockchain for internet of things a survey"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"

Connected through hyperedge HE-2 (induced by source paper "optimal fairness aware time and power allocation in wireless powered communication networks"):
  with members:
    - "wireless networks with energy harvesting and power transfer joint power and time allocation"
    - "wireless powered communication networks an overview"
    - "artificial noise aided secrecy information and power transfer in ofdma systems"
    - "wireless powered communication opportunities and challenges"
    - "energy harvesting for physical layer security in ofdma networks"
    - "simultaneous wireless information and power transfer in modern communication systems"
    - "throughput maximization in wireless powered communication networks"
    - "asymptotically optimal power allocation for energy harvesting communication networks"

Connected through hyperedge HE-3 (induced by source paper "power beacon assisted millimeter wave ad hoc networks"):
  with members:
    - "modeling and analyzing millimeter wave cellular systems"
    - "performance analysis of outdoor mmwave ad hoc networks"
    - "device to device millimeter wave communications interference coverage rate and finite topologies"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "communications and signals design for wireless power transmission"
    - "ambient rf energy harvesting in ultra dense small cell networks performance and trade offs"
    - "millimeter wave energy harvesting"
    - "stochastic geometry modeling and analysis of multi tier millimeter wave cellular networks"

Connected through hyperedge HE-4 (induced by source paper "theory and experiment for wireless powered sensor networks how to keep sensors alive"):
  with members:
    - "experiment modeling and analysis of wireless powered sensor network for energy neutral power management"
    - "distributed charging control in broadband wireless power transfer networks"
    - "communications and signals design for wireless power transmission"
    - "multiuser mimo wireless energy transfer with coexisting opportunistic communication"
    - "joint beamforming design for multi user wireless information and power transfer"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"
    - "application of smart antenna technologies in simultaneous wireless information and power transfer"
    - "multi antenna wireless powered communication with energy beamforming"

Connected through hyperedge HE-5 (induced by source paper "theory and experiment for wireless powered sensor networks how to keep multiple sensor nodes alive"):
  with members:
    - "experiment modeling and analysis of wireless powered sensor network for energy neutral power management"
    - "distributed charging control in broadband wireless power transfer networks"
    - "joint beamforming design for multi user wireless information and power transfer"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"
    - "multi antenna wireless powered communication with energy beamforming"

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)