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: "online learning schemes for power allocation in energy harvesting communications"
  Abstract: We consider the problem of power allocation over a time-varying channel with unknown distribution in energy harvesting communication systems. In this problem, the transmitter has to choose the transmit power based on the amount of stored energy in its battery with the goal of maximizing the average rate obtained over time. We model this problem as a Markov decision process (MDP) with the transmitter as the agent, the battery status as the state, the transmit power as the action and the rate obtained as the reward. The average reward maximization problem over the MDP can be solved by a linear program (LP) that uses the transition probabilities for the state-action pairs and their reward values to choose a power allocation policy. Since the rewards associated the state-action pairs are unknown, we propose two online learning algorithms: UCLP and Epoch-UCLP that learn these rewards and adapt their policies along the way. The UCLP algorithm solves the LP at each step to decide its current policy using the upper confidence bounds on the rewards, while the Epoch-UCLP algorithm divides the time into epochs, solves the LP only at the beginning of the epochs and follows the obtained policy in that epoch. We prove that the reward losses or regrets incurred by both these algorithms are upper bounded by constants. Epoch-UCLP incurs a higher regret compared to UCLP, but reduces the computational requirements substantially. We also show that the presented algorithms work for online learning in cost minimization problems like the packet scheduling with power-delay tradeoff with minor changes.

Connected through hyperedge HE-1 (induced by source paper "learning and fairness in energy harvesting a maximin multi armed bandits approach"):
  with members:
    - "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"
    - "energy harvesting wireless communications a review of recent advances"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "user cooperation in wireless powered communication networks"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "throughput maximization in wireless powered communication networks"

Connected through hyperedge HE-2 (induced by source paper "resource allocation in wireless powered iot networks"):
  with members:
    - "low power wide area networks for sustainable iot"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"
    - "wireless powered communication networks an overview"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "throughput maximization in wireless powered communication networks"
    - "multi antenna wireless powered communication with energy beamforming"
    - "peer effects and stability in matching markets"

Connected through hyperedge HE-3 (induced by source paper "optimal power control for transmitting correlated sources with energy harvesting constraints"):
  with members:
    - "optimized sensor collaboration for estimation of temporally correlated parameters"
    - "energy harvesting wireless communications a review of recent advances"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "the binary energy harvesting channel with a unit sized battery"
    - "on online energy harvesting in multiple access communication systems"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "estimation diversity and energy efficiency in distributed sensing"

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.LG(Machine Learning)