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 the optimality of myopic sensing in multi channel opportunistic access the case of sensing multiple channels"
  Abstract: Recent works have developed a simple and robust myopic sensing policy for multi-channel opportunistic communication systems where a secondary user (SU) can access one of N i.i.d. Markovian channels. The optimality of the myopic sensing policy in maximizing the SU's cumulated reward is established under certain conditions on channel parameters. This paper studies the generic case where the SU can sense more than one channel each time. By characterizing the myopic sensing policy in this context, we establish analytically its optimality for certain system setting when the SU is allowed to sense two channels. In the more generic case, we construct counterexamples to show that the myopic sensing policy, despite its simple structure, is non-optimal.

Connected through hyperedge HE-1 (induced by source paper "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"):
  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-2 (induced by source paper "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "on optimality of greedy policy for a class of standard reward function of restless multi armed bandit problem"):
  with members:
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "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)