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: "movers and shakers kinetic energy harvesting for the internet of things"
  Abstract: Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.

Connected through hyperedge HE-1 (induced by source paper "capacitor based activity sensing for kinetic powered wearable iots"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"

Connected through hyperedge HE-2 (induced by source paper "visible light communication for wearable computing"):
  with members:
    - "passive communication with ambient light"

Connected through hyperedge HE-3 (induced by source paper "maximizing broadcast throughput under ultra low power constraints"):
  with members:
    - "robust neighbor discovery in multi hop multi channel heterogeneous wireless networks"
    - "panda neighbor discovery on a power harvesting budget"
    - "energy harvesting wireless communications a review of recent advances"
    - "on heterogeneous neighbor discovery in wireless sensor networks"
    - "queue based random access algorithms fluid limits and stability issues"
    - "on the design of efficient csma algorithms for wireless networks"

Connected through hyperedge HE-4 (induced by source paper "a stochastic geometry analysis of large scale cooperative wireless networks powered by energy harvesting"):
  with members:
    - "millimeter wave energy harvesting"
    - "on the stability of random multiple access with stochastic energy harvesting"
    - "energy harvesting wireless communications a review of recent advances"
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "spectral efficiency of dynamic coordinated beamforming a stochastic geometry approach"
    - "stability analysis of slotted aloha with opportunistic rf energy harvesting"
    - "a tractable model for noncoherent joint transmission base station cooperation"
    - "simultaneous information and energy transfer in large scale networks with without relaying"

Connected through hyperedge HE-5 (induced by source paper "energy harvesting framework for network simulator 3 ns 3"):
  with members:
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal energy management policies for energy harvesting sensor nodes"

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.ET(Emerging Technologies)