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 "learning and fairness in energy harvesting a maximin multi armed bandits approach"):
  Abstract: Recent advances in wireless radio frequency (RF) energy harvesting allows sensor nodes to increase their lifespan by remotely charging their batteries. The amount of energy harvested by the nodes varies depending on their ambient environment, and proximity to the source and lifespan of the sensor network depends on the minimum amount of energy a node can harvest in the network. It is thus important to learn the least amount of energy harvested by nodes so that the source can transmit on a frequency band that maximizes this amount. We model this learning problem as a novel stochastic \textit{Maximin Multi-Armed Bandits} (Maximin MAB) problem and propose an Upper Confidence Bound (UCB) based algorithm named Maximax UCB. Maximin MAB is a generalization of standard MAB and enjoys the same performance guarantee as to the UCB1 algorithm. Experimental results validate the performance guarantees of our algorithm.

Includes members:
  Member "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "risk averse explore then commit algorithms for finite time bandits")
  Member "online learning schemes for power allocation in energy harvesting communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resource allocation in wireless powered iot networks")
  Member "energy harvesting wireless communications a review of recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "throughput optimization for massive mimo systems powered by wireless energy transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "user cooperation in wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "common throughput maximization for uav enabled interference channel with wireless powered communications")
  Member "wireless networks with rf energy harvesting a contemporary survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "throughput maximization in wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on maximizing information reliability in wireless powered cooperative networks")

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.LG(Machine Learning)