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: "improved path length regret bounds for bandits"
  Abstract: We study adaptive regret bounds in terms of the variation of the losses (the so-called path-length bounds) for both multi-armed bandit and more generally linear bandit. We first show that the seemingly suboptimal path-length bound of (Wei and Luo, 2018) is in fact not improvable for adaptive adversary. Despite this negative result, we then develop two new algorithms, one that strictly improves over (Wei and Luo, 2018) with a smaller path-length measure, and the other which improves over (Wei and Luo, 2018) for oblivious adversary when the path-length is large. Our algorithms are based on the well-studied optimistic mirror descent framework, but importantly with several novel techniques, including new optimistic predictions, a slight bias towards recently selected arms, and the use of a hybrid regularizer similar to that of (Bubeck et al., 2018). #R##N#Furthermore, we extend our results to linear bandit by showing a reduction to obtaining dynamic regret for a full-information problem, followed by a further reduction to convex body chasing. We propose a simple greedy chasing algorithm for squared 2-norm, leading to new dynamic regret results and as a consequence the first path-length regret for general linear bandit as well.

Connected through hyperedge HE-1 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "competing bandits the perils of exploration under competition"
    - "factored bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "bandit regret scaling with the effective loss range"
    - "competing bandits learning under competition"

Connected through hyperedge HE-2 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback"):
  with members:
    - "equipping experts bandits with long term memory"
    - "first order regret analysis of thompson sampling"
    - "bandits with feedback graphs and switching costs"
    - "online learning with feedback graphs and switching costs"
    - "make the minority great again first order regret bound for contextual bandits"
    - "online learning with graph structured feedback against adaptive adversaries"
    - "small loss bounds for online learning with partial information"
    - "sparsity variance and curvature in multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "taking a hint how to leverage loss predictors in contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "fast convergence of regularized learning in games"
    - "strongly adaptive online learning"

Connected through hyperedge HE-4 (induced by source paper "adaptive and efficient algorithms for tracking the best expert"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "mirror descent meets fixed share and feels no regret"
    - "near optimal algorithms for online matrix prediction"

Connected through hyperedge HE-5 (induced by source paper "model free reinforcement learning in infinite horizon average reward markov decision processes"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "regret minimization for reinforcement learning by evaluating the optimal bias function"
    - "near optimal exploration exploitation in non communicating markov decision processes"
    - "more adaptive algorithms for adversarial bandits"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"

Connected through hyperedge HE-6 (induced by source paper "exploration by optimisation in partial monitoring"):
  with members:
    - "on first order bounds variance and gap dependent bounds for adversarial bandits"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "connections between mirror descent thompson sampling and the information ratio"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "more adaptive algorithms for adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"

Connected through hyperedge HE-7 (induced by source paper "path length bounds for gradient descent and flow"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "a nearly linear bound for chasing nested convex bodies"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "more adaptive algorithms for adversarial bandits"
    - "chasing nested convex bodies nearly optimally"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-8 (induced by source paper "online optimization with predictions and non convex losses"):
  with members:
    - "chasing convex bodies optimally"
    - "introduction to online convex optimization"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "potential function proofs for first order methods"
    - "k server via multiscale entropic regularization"

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)