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: "more adaptive algorithms for adversarial bandits"
  Abstract: We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem). When instantiated differently, our algorithm achieves various new data-dependent regret bounds improving previous works. Examples include: 1) a regret bound depending on the variance of only the best arm; 2) a regret bound depending on the first-order path-length of only the best arm; 3) a regret bound depending on the sum of first-order path-lengths of all arms as well as an important negative term, which together lead to faster convergence rates for some normal form games with partial feedback; 4) a regret bound that simultaneously implies small regret when the best arm has small loss and logarithmic regret when there exists an arm whose expected loss is always smaller than those of others by a fixed gap (e.g. the classic i.i.d. setting). In some cases, such as the last two results, our algorithm is completely parameter-free. #R##N#The main idea of our algorithm is to apply the optimism and adaptivity techniques to the well-known Online Mirror Descent framework with a special log-barrier regularizer. The challenges are to come up with appropriate optimistic predictions and correction terms in this framework. Some of our results also crucially rely on using a sophisticated increasing learning rate schedule.

Connected through hyperedge HE-1 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "improved path length regret bounds for bandits"
    - "competing bandits the perils of exploration under competition"
    - "factored bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "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 "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"
    - "improved path length regret bounds for 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-3 (induced by source paper "nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "collaborative filtering bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-4 (induced by source paper "adaptive and efficient algorithms for tracking the best expert"):
  with members:
    - "improved path length regret bounds for 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 "corruption robust exploration in episodic reinforcement learning"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "recent advances in algorithmic high dimensional robust statistics"

Connected through hyperedge HE-6 (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"
    - "improved path length regret bounds for bandits"
    - "near optimal exploration exploitation in non communicating markov decision processes"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"

Connected through hyperedge HE-7 (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"
    - "improved path length regret bounds for bandits"
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "sparsity variance and curvature in multi armed bandits"

Connected through hyperedge HE-8 (induced by source paper "bandit principal component analysis"):
  with members:
    - "bilinear bandits with low rank structure"
    - "introduction to online convex optimization"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "sparsity variance and curvature in multi armed bandits"
    - "stochastic low rank bandits"
    - "corralling a band of bandit algorithms"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"

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)