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: "bistro an efficient relaxation based method for contextual bandits"
  Abstract: We present efficient algorithms for the problem of contextual bandits with i.i.d. covariates, an arbitrary sequence of rewards, and an arbitrary class of policies. Our algorithm BISTRO requires d calls to the empirical risk minimization (ERM) oracle per round, where d is the number of actions. The method uses unlabeled data to make the problem computationally simple. When the ERM problem itself is computationally hard, we extend the approach by employing multiplicative approximation algorithms for the ERM. The integrality gap of the relaxation only enters in the regret bound rather than the benchmark. Finally, we show that the adversarial version of the contextual bandit problem is learnable (and efficient) whenever the full-information supervised online learning problem has a non-trivial regret guarantee (and efficient).

Connected through hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles"):
  with members:
    - "efficient and robust algorithms for adversarial linear contextual bandits"
    - "introduction to online convex optimization"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "new insights into bootstrapping for bandits"
    - "practical contextual bandits with regression oracles"

Connected through hyperedge HE-2 (induced by source paper "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "online optimization competing with dynamic comparators"
    - "the computational power of optimization in online learning"

Connected through hyperedge HE-3 (induced by source paper "bandit multiclass linear classification efficient algorithms for the separable case"):
  with members:
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "logistic regression the importance of being improper"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "learning depth three neural networks in polynomial time"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "the price of bandit information in multiclass online classification"

Connected through hyperedge HE-4 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

Connected through hyperedge HE-5 (induced by source paper "small loss bounds for online learning with partial information"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with feedback graphs without the graphs"
    - "learning in games robustness of fast convergence"
    - "efficient algorithms for adversarial contextual learning"
    - "learning and efficiency in games with dynamic population"
    - "strongly adaptive online learning"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-6 (induced by source paper "efficient contextual bandits in non stationary worlds"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "strongly adaptive regret implies optimally dynamic regret"
    - "corralling a band of bandit algorithms"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-7 (induced by source paper "oracle efficient online learning and auction design"):
  with members:
    - "online learning with low rank experts"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "the computational power of optimization in online learning"
    - "ironing in the dark"
    - "learning in auctions regret is hard envy is easy"

Connected through hyperedge HE-8 (induced by source paper "efficient algorithms for adversarial contextual learning"):
  with members:
    - "fast convergence of regularized learning in games"
    - "learning in auctions regret is hard envy is easy"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "optimization learning and games with predictable sequences"
    - "an efficient algorithm for learning with semi bandit feedback"
    - "efficient optimal learning for contextual bandits"
    - "efficient tracking of large classes of experts"
    - "adaptive online prediction by following the perturbed leader"

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)