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: "a learning theoretic approach to energy harvesting communication system optimization"
  Abstract: A point-to-point wireless communication system in which the transmitter is equipped with an energy harvesting device and a rechargeable battery, is studied. Both the energy and the data arrivals at the transmitter are modeled as Markov processes. Delay-limited communication is considered assuming that the underlying channel is block fading with memory, and the instantaneous channel state information is available at both the transmitter and the receiver. The expected total transmitted data during the transmitter's activation time is maximized under three different sets of assumptions regarding the information available at the transmitter about the underlying stochastic processes. A learning theoretic approach is introduced, which does not assume any a priori information on the Markov processes governing the communication system. In addition, online and offline optimization problems are studied for the same setting. Full statistical knowledge and causal information on the realizations of the underlying stochastic processes are assumed in the online optimization problem, while the offline optimization problem assumes non-causal knowledge of the realizations in advance. Comparing the optimal solutions in all three frameworks, the performance loss due to the lack of the transmitter's information regarding the behaviors of the underlying Markov processes is quantified.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "a maximin optimal online power control policy for energy harvesting communications"):
  with members:
    - "the optimal power control policy for an energy harvesting system with look ahead bernoulli energy arrivals"
    - "on the optimality of the greedy policy for battery limited energy harvesting communications"
    - "energy harvesting wireless communications a review of recent advances"
    - "universally near optimal online power control for energy harvesting nodes"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "throughput optimal policies for energy harvesting wireless transmitters with non ideal circuit power"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-3 (induced by source paper "on the optimality of the greedy policy for battery limited energy harvesting communications"):
  with members:
    - "the optimal power control policy for an energy harvesting system with look ahead bernoulli energy arrivals"
    - "energy harvesting wireless communications a review of recent advances"
    - "universally near optimal online power control for energy harvesting nodes"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "throughput optimal policies for energy harvesting wireless transmitters with non ideal circuit power"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimum transmission policies for battery limited energy harvesting nodes"

Connected through hyperedge HE-4 (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"
    - "accelerated structure aware reinforcement learning for delay sensitive energy harvesting wireless sensors"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "harvest or transmit policy for cognitive radio networks a learning theoretic approach"):
  with members:
    - "spectrum access in cognitive radio using a two stage reinforcement learning approach"
    - "optimal harvest or transmit strategy for energy harvesting underlay cognitive radio network"
    - "end to end throughput maximization for underlay multi hop cognitive radio networks with rf energy harvesting"
    - "online time sharing policy in energy harvesting cognitive radio network with channel uncertainty"
    - "on optimal offline time sharing policy for energy harvesting underlay cognitive radio"
    - "on energy cooperation in energy harvesting underlay cognitive radio network"
    - "an optimal transmission policy for energy harvesting systems with continuous energy and data arrivals"
    - "optimal time sharing in underlay cognitive radio systems with rf energy harvesting"

Connected through hyperedge HE-6 (induced by source paper "jam me if you can defeating jammer with deep dueling neural network architecture and ambient backscattering augmented communications"):
  with members:
    - "ambient backscatter communications a contemporary survey"
    - "anti jamming communications using spectrum waterfall a deep reinforcement learning approach"
    - "cooperative access schemes for efficient swipt transmissions in cognitive radio networks"
    - "learning both weights and connections for efficient neural networks"
    - "dueling network architectures for deep reinforcement learning"
    - "in band full duplex wireless challenges and opportunities"
    - "on the capacity of rate adaptive packetized wireless communication links under jamming"

Connected through hyperedge HE-7 (induced by source paper "communication over a time correlated channel with an energy harvesting transmitter"):
  with members:
    - "energy harvesting wireless networks with correlated energy sources"
    - "channel sensing and communication over a time correlated channel with an energy harvesting transmitter"
    - "distributed opportunistic scheduling for energy harvesting based wireless networks a two stage probing approach"
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "data driven stochastic models and policies for energy harvesting sensor communications"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"

Connected through hyperedge HE-8 (induced by source paper "energy harvesting wireless networks with correlated energy sources"):
  with members:
    - "distributed opportunistic scheduling for energy harvesting based wireless networks a two stage probing approach"
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "optimal packet scheduling on an energy harvesting broadcast link"

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)