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: "multi channel opportunistic access a case of restless bandits with multiple plays"
  Abstract: This paper considers the following stochastic control problem that arises in opportunistic spectrum access: a system consists of n channels (Gilbert-Elliot channels)where the state (good or bad) of each channel evolves as independent and identically distributed Markov processes. A user can select exactly k channels to sense and access (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a good channel. The objective is to design a channel selection policy that maximizes the expected discounted total reward accrued over a finite or infinite horizon. In our previous work we established the optimality of a greedy policy for the special case of k = 1 (i.e., single channel access) under the condition that the channel state transitions are positively correlated over time. In this paper we show under the same condition the greedy policy is optimal for the general case of k >= 1; the methodology introduced here is thus more general. This problem may be viewed as a special case of the restless bandit problem, with multiple plays. We discuss connections between the current problem and existing literature on this class of problems.

Connected through hyperedge HE-1 (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"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-2 (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"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "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"
    - "cognitive medium access exploration exploitation and competition"

Connected through hyperedge HE-3 (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"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "optimal packet scheduling in an energy harvesting communication system"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-4 (induced by source paper "the cognitive compressive sensing problem"):
  with members:
    - "compressive link acquisition in multiuser communications"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling of short pulses"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "algorithms for dynamic spectrum access with learning for cognitive radio"

Connected through hyperedge HE-5 (induced by source paper "urop a simple near optimal scheduling policy for energy harvesting sensors"):
  with members:
    - "optimality of myopic sensing in multi channel opportunistic access"

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 policy for restless multi armed bandit problem an axiomatic approach"
    - "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access"
    - "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 "security of spectrum learning in cognitive radios"):
  with members:
    - "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"
    - "cognitive medium access exploration exploitation and competition"

Connected through hyperedge HE-8 (induced by source paper "on the optimality of myopic sensing in multi state channels"):
  with members:
    - "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"

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.IT(Information Theory)