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: "accelerated structure aware reinforcement learning for delay sensitive energy harvesting wireless sensors"
  Abstract: We investigate an energy-harvesting wireless sensor transmitting latency-sensitive data over a fading channel. The sensor injects captured data packets into its transmission queue and relies on ambient energy harvested from the environment to transmit them. We aim to find the optimal scheduling policy that decides whether or not to transmit the queue's head-of-line packet at each transmission opportunity such that the expected packet queuing delay is minimized given the available harvested energy. No prior knowledge of the stochastic processes that govern the channel, captured data, or harvested energy dynamics are assumed, thereby necessitating the use of online learning to optimize the scheduling policy. We formulate this scheduling problem as a Markov decision process (MDP) and analyze the structural properties of its optimal value function. In particular, we show that it is non-decreasing and has increasing differences in the queue backlog and that it is non-increasing and has increasing differences in the battery state. We exploit this structure to formulate a novel accelerated reinforcement learning (RL) algorithm to solve the scheduling problem online at a much faster learning rate, while limiting the induced computational complexity. Our experiments demonstrate that the proposed algorithm closely approximates the performance of an optimal offline solution that requires a priori knowledge of the channel, captured data, and harvested energy dynamics. Simultaneously, by leveraging the value function's structure, our approach achieves competitive performance relative to a state-of-the-art RL algorithm, at potentially orders of magnitude lower complexity. Finally, considerable performance gains are demonstrated over the well-known Q-learning algorithm.

Connected through hyperedge HE-1 (induced by source paper "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"):
  with members:
    - "progressive weight pruning of deep neural networks using admm"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "multi agent reinforcement learning for energy harvesting two hop communications with full cooperation"
    - "distributed power control for delay optimization in energy harvesting cooperative relay networks"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "dynamic network surgery for efficient dnns"
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "iterative dynamic water filling for fading multiple access channels with energy harvesting"

Connected through hyperedge HE-2 (induced by source paper "delay optimal and energy efficient communications with markovian arrivals"):
  with members:
    - "joint queue aware and channel aware delay optimal scheduling of arbitrarily bursty traffic over multi state time varying channels"
    - "delay optimal probabilistic scheduling in green communications with arbitrary arrival and adaptive transmission"
    - "delay optimal buffer aware scheduling with adaptive transmission"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"

Connected through hyperedge HE-3 (induced by source paper "online reinforcement learning of optimal threshold policies for markov decision processes"):
  with members:
    - "a structure aware online learning algorithm for markov decision processes"

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.NI(Networking and Internet Architecture)