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 "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.

Includes members:
  Member "stochastic contextual bandits with known reward functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online learning with diverse user preferences")
  Member "energy harvesting wireless communications a review of recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "efficient optimal learning for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "transmission with energy harvesting nodes in fading wireless channels optimal policies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "closed form analysis of non linear age of information in status updates with an energy harvesting transmitter")
  Member "optimum transmission policies for battery limited energy harvesting nodes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")
  Member "optimal packet scheduling in an energy harvesting communication system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")
  Member "a contextual bandit approach to personalized news article recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)