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: "explore no more improved high probability regret bounds for non stochastic bandits"
  Abstract: This work addresses the problem of regret minimization in non-stochastic multi-armed bandit problems, focusing on performance guarantees that hold with high probability. Such results are rather scarce in the literature since proving them requires a large deal of technical effort and significant modifications to the standard, more intuitive algorithms that come only with guarantees that hold on expectation. One of these modifications is forcing the learner to sample arms from the uniform distribution at least $\Omega(\sqrt{T})$ times over $T$ rounds, which can adversely affect performance if many of the arms are suboptimal. While it is widely conjectured that this property is essential for proving high-probability regret bounds, we show in this paper that it is possible to achieve such strong results without this undesirable exploration component. Our result relies on a simple and intuitive loss-estimation strategy called Implicit eXploration (IX) that allows a remarkably clean analysis. To demonstrate the flexibility of our technique, we derive several improved high-probability bounds for various extensions of the standard multi-armed bandit framework. Finally, we conduct a simple experiment that illustrates the robustness of our implicit exploration technique.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "learning to optimize under non stationarity"
    - "iterate averaging as regularization for stochastic gradient descent"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "make the minority great again first order regret bound for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "optimistic policy optimization with bandit feedback"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on connections between constrained optimization and reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "estimating learnability in the sublinear data regime"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"

Connected through hyperedge HE-4 (induced by source paper "learning adversarial mdps with bandit feedback and unknown transition"):
  with members:
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "online convex optimization in adversarial markov decision processes"
    - "corruption robust exploration in episodic reinforcement learning"
    - "reinforcement learning under drift"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "regret minimization for reinforcement learning by evaluating the optimal bias function"
    - "drifting reinforcement learning the blessing of more optimism in face of endogenous exogenous dynamics"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "minimax optimal algorithms for adversarial bandit problem with multiple plays"):
  with members:
    - "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"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "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-6 (induced by source paper "online learning with abstention"):
  with members:
    - "introduction to online convex optimization"
    - "online learning with feedback graphs without the graphs"
    - "the extended littlestone s dimension for learning with mistakes and abstentions"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "from bandits to experts a tale of domination and independence"
    - "leveraging side observations in stochastic bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "delay and cooperation in nonstochastic bandits"):
  with members:
    - "multi player bandits a musical chairs approach"
    - "parallel correlation clustering on big graphs"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "distributed online learning in social recommender systems"
    - "online learning under delayed feedback"
    - "distributed online learning via cooperative contextual bandits"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-8 (induced by source paper "an efficient high probability algorithm for linear bandits"):
  with members:
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "combinatorial bandits revisited"
    - "tight bounds for approximate carath eodory and beyond"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "how to beat the adaptive multi armed bandit"

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)