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 optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
  Abstract: Due to its application in numerous engineering problems, the restless multi-armed bandit (RMAB) problem is of fundamental importance in stochastic decision theory. However, solving the RMAB problem is well known to be PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. A natural alternative approach is to seek simple myopic policies which are easy to implement. This paper presents a generic study on the optimality of the myopic policy for the RMAB problem. More specifically, we develop three axioms characterizing a family of generic and practically important functions termed as regular functions. By performing a mathematical analysis based on the developed axioms, we establish the closed-form conditions under which the myopic policy is guaranteed to be optimal. The axiomatic analysis also illuminates important engineering implications of the myopic policy including the intrinsic tradeoff between exploration and exploitation. A case study is then presented to illustrate the application of the derived results in analyzing a class of RMAB problems arising from multi-channel opportunistic access.

Connected through hyperedge HE-1 (induced by source paper "minimax optimal algorithms for adversarial bandit problem with multiple plays"):
  with members:
    - "explore no more improved high probability regret bounds for non stochastic bandits"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "feedback solution to optimal switching problems with switching cost"
    - "distributed online learning via cooperative contextual bandits"
    - "regret in online combinatorial optimization"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-2 (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"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-3 (induced by source paper "deep multi user reinforcement learning for distributed dynamic spectrum access"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "approximate best response dynamics in random interference games"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"
    - "active anomaly detection in heterogeneous processes"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"

Connected through hyperedge HE-5 (induced by source paper "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "optimal packet scheduling in an energy harvesting communication system"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-6 (induced by source paper "sufficient conditions on the optimality of myopic sensing in opportunistic channel access a unifying framework"):
  with members:
    - "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "approximation algorithms for restless bandit problems"

Connected through hyperedge HE-7 (induced by source paper "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access"):
  with members:
    - "on the optimality of myopic sensing in multi channel opportunistic access the case of sensing multiple channels"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "joint design and separation principle for opportunistic spectrum access in the presence of sensing errors"
    - "approximation algorithms for restless bandit problems"

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.SY(Systems and Control)