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: "decentralized online learning algorithms for opportunistic spectrum access"
  Abstract: The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are $M$ users and $N$ arms (channels) that each offer i.i.d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design a decentralized online learning policy that incurs minimal regret, defined as the difference between the total expected rewards accumulated by a model-aware genie, and that obtained by all users applying the policy. We make two contributions in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the $K$-th-ranked user to learn to access the arm offering the $K$-th highest mean reward. For this problem, we present the first distributed policy that yields regret that is uniformly logarithmic over time without requiring any prior assumption about the mean rewards. Second, we consider the case when a fair access policy is required, i.e., it is desired for all users to experience the same mean reward. For this problem, we present a distributed policy that yields order-optimal regret scaling with respect to the number of users and arms, better than previously proposed policies in the literature. Both of our distributed policies make use of an innovative modification of the well known UCB1 policy for the classic multi-armed bandit problem that allows a single user to learn how to play the arm that yields the $K$-th largest mean reward.

Connected through hyperedge HE-1 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"):
  with members:
    - "data poisoning attacks on stochastic bandits"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "algorithms for multi armed bandit problems"
    - "the best of both worlds stochastic and adversarial bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "a distributed learning dynamics in social groups"):
  with members:
    - "ant inspired density estimation via random walks"
    - "human collective intelligence as distributed bayesian inference"
    - "irls and slime mold equivalence and convergence"
    - "distributed house hunting in ant colonies"
    - "stabilizing consensus with many opinions"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "online distributed sensor selection"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-3 (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"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-4 (induced by source paper "towards distribution free multi armed bandits with combinatorial strategies"):
  with members:
    - "online learning in decentralized multiuser resource sharing problems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "dynamic pricing with limited supply"
    - "truthful mechanisms with implicit payment computation"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-5 (induced by source paper "almost optimal channel access in multi hop networks with unknown channel variables"):
  with members:
    - "online learning in decentralized multiuser resource sharing problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-6 (induced by source paper "online learning algorithms for stochastic water filling"):
  with members:
    - "online learning for combinatorial network optimization with restless markovian rewards"

Connected through hyperedge HE-7 (induced by source paper "extended ucb policy for multi armed bandit with light tailed reward distributions"):
  with members:
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

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.LG(Machine Learning)