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: "cognitive medium access exploration exploitation and competition"
  Abstract: This paper establishes the equivalence between cognitive medium access and the competitive multi-armed bandit problem. First, the scenario in which a single cognitive user wishes to opportunistically exploit the availability of empty frequency bands in the spectrum with multiple bands is considered. In this scenario, the availability probability of each channel is unknown to the cognitive user a priori. Hence efficient medium access strategies must strike a balance between exploring the availability of other free channels and exploiting the opportunities identified thus far. By adopting a Bayesian approach for this classical bandit problem, the optimal medium access strategy is derived and its underlying recursive structure is illustrated via examples. To avoid the prohibitive computational complexity of the optimal strategy, a low complexity asymptotically optimal strategy is developed. The proposed strategy does not require any prior statistical knowledge about the traffic pattern on the different channels. Next, the multi-cognitive user scenario is considered and low complexity medium access protocols, which strike the optimal balance between exploration and exploitation in such competitive environments, are developed. Finally, this formalism is extended to the case in which each cognitive user is capable of sensing and using multiple channels simultaneously.

Connected through hyperedge HE-1 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense"):
  with members:
    - "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"
    - "data poisoning attacks on stochastic bandits"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "stochastic bandits robust to adversarial corruptions"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-2 (induced by source paper "observe before play multi armed bandit with pre observations"):
  with members:
    - "cost aware cascading bandits"
    - "multi armed bandit learning in iot networks learning helps even in non stationary settings"
    - "cascading bandits for large scale recommendation problems"
    - "multi user lax communications a multi armed bandit approach"
    - "multi player bandits a musical chairs approach"
    - "combinatorial cascading bandits"
    - "cascading bandits learning to rank in the cascade model"
    - "concurrent bandits and cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "multiplayer multi armed bandits for optimal assignment in heterogeneous networks"):
  with members:
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "multi player bandits a trekking approach"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "further optimal regret bounds for thompson sampling"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-6 (induced by source paper "quick best action identification in linear bandit problems"):
  with members:
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-7 (induced by source paper "toward optimal adaptive wireless communications in unknown environments"):
  with members:
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "almost optimal channel access in multi hop networks with unknown channel variables"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "decentralized online learning algorithms for opportunistic spectrum access"

Connected through hyperedge HE-8 (induced by source paper "amazon in the white space social recommendation aided distributed spectrum access"):
  with members:
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "imitation based social spectrum sharing"
    - "spatial spectrum access game"
    - "smart streaming for online video services"
    - "adaptive channel recommendation for opportunistic spectrum access"

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)