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: "contextual bandit algorithms with supervised learning guarantees"
  Abstract: We address the problem of learning in an online, bandit setting where the learner must repeatedly select among $K$ actions, but only receives partial feedback based on its choices. We establish two new facts: First, using a new algorithm called Exp4.P, we show that it is possible to compete with the best in a set of $N$ experts with probability $1-\delta$ while incurring regret at most $O(\sqrt{KT\ln(N/\delta)})$ over $T$ time steps. The new algorithm is tested empirically in a large-scale, real-world dataset. Second, we give a new algorithm called VE that competes with a possibly infinite set of policies of VC-dimension $d$ while incurring regret at most $O(\sqrt{T(d\ln(T) + \ln (1/\delta))})$ with probability $1-\delta$. These guarantees improve on those of all previous algorithms, whether in a stochastic or adversarial environment, and bring us closer to providing supervised learning type guarantees for the contextual bandit setting.

Connected through hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"
    - "on exact computation with an infinitely wide neural net"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "what can resnet learn efficiently going beyond kernels"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"

Connected through hyperedge HE-2 (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-3 (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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "contextual blocking bandits"):
  with members:
    - "variational regret bounds for reinforcement learning"
    - "blocking bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "discrepancy based algorithms for non stationary rested bandits"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "stochastic linear contextual bandits with diverse contexts"):
  with members:
    - "budget constrained edge service provisioning with demand estimation via bandit learning"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "finite time analysis of kernelised contextual bandits"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-7 (induced by source paper "on the generalization gap in reparameterizable reinforcement learning"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "reinforcement learning with perturbed rewards"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "identifying generalization properties in neural networks"
    - "generalization and regularization in dqn"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "convergence and margin of adversarial training on separable data"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "does data augmentation lead to positive margin"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "universal adversarial training"
    - "adversarially robust generalization requires more data"

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)