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: "on myopic sensing for multi channel opportunistic access structure optimality and performance"
  Abstract: We consider a multi-channel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the Gilbert- Elliot channel model). A user chooses one channel to sense and access in each slot and collects a reward determined by the state of the chosen channel. The problem is to design a sensing policy for channel selection to maximize the average reward, which can be formulated as a multi-arm restless bandit process. In this paper, we study the structure, optimality, and performance of the myopic sensing policy. We show that the myopic sensing policy has a simple robust structure that reduces channel selection to a round-robin procedure and obviates the need for knowing the channel transition probabilities. The optimality of this simple policy is established for the two-channel case and conjectured for the general case based on numerical results. The performance of the myopic sensing policy is analyzed, which, based on the optimality of myopic sensing, characterizes the maximum throughput of a multi-channel opportunistic communication system and its scaling behavior with respect to the number of channels. These results apply to cognitive radio networks, opportunistic transmission in fading environments, downlink scheduling in centralized networks, and resource-constrained jamming and anti-jamming.

Connected through hyperedge HE-1 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-2 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-3 (induced by source paper "a deep actor critic reinforcement learning framework for dynamic multichannel access"):
  with members:
    - "reinforcement learning based resource allocation in fog radio access networks for various iot environments"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-4 (induced by source paper "learning in restless multi armed bandits via adaptive arm sequencing rules"):
  with members:
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "satisficing in multi armed bandit problems"
    - "distributed game theoretic optimization and management of multichannel aloha networks"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for dynamic multichannel access in wireless networks"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "data efficient learning of feedback policies from image pixels using deep dynamical models"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "multiple object recognition with visual attention"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "cost aware learning and optimization for opportunistic spectrum access"):
  with members:
    - "an online approach to dynamic channel access and transmission scheduling"
    - "cascading bandits learning to rank in the cascade model"
    - "further optimal regret bounds for thompson sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "joint design and separation principle for opportunistic spectrum access in the presence of sensing errors"
    - "cognitive medium access exploration exploitation and competition"

Connected through hyperedge HE-7 (induced by source paper "sequential decision making with limited observation capability application to wireless networks"):
  with members:
    - "a hidden markov restless multi armed bandit model for playout recommendation systems"
    - "opportunistic scheduling as restless bandits"
    - "multiuser scheduling in a markov modeled downlink using randomly delayed arq feedback"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "downlink scheduling over markovian fading channels"
    - "optimality of myopic sensing in multi channel opportunistic access"

Connected through hyperedge HE-8 (induced by source paper "actor critic deep reinforcement learning for dynamic multichannel access"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"

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)