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: "bandits and experts in metric spaces"
  Abstract: In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active investigation, motivated by practical applications such as online auctions and web advertisement. The goal of such research is to identify broad and natural classes of strategy sets and payoff functions which enable the design of efficient solutions. #R##N#In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the "Lipschitz MAB problem". We present a solution for the multi-armed bandit problem in this setting. That is, for every metric space we define an isometry invariant which bounds from below the performance of Lipschitz MAB algorithms for this metric space, and we present an algorithm which comes arbitrarily close to meeting this bound. Furthermore, our technique gives even better results for benign payoff functions. We also address the full-feedback ("best expert") version of the problem, where after every round the payoffs from all arms are revealed.

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 "advances in bandits with knapsacks"):
  with members:
    - "introduction to multi armed bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "adversarial bandits with knapsacks"
    - "combinatorial semi bandits with knapsacks"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "efficient learning in large scale combinatorial semi bandits"
    - "resourceful contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "on thompson sampling for smoother than lipschitz bandits"):
  with members:
    - "thompson sampling and approximate inference"
    - "introduction to multi armed bandits"
    - "thompson sampling for combinatorial semi bandits"
    - "a tutorial on thompson sampling"
    - "learning to optimize via posterior sampling"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"

Connected through hyperedge HE-4 (induced by source paper "contextual search for general hypothesis classes"):
  with members:
    - "corrupted multidimensional binary search learning in the presence of irrational agents"
    - "contextual search via intrinsic volumes"
    - "online learning with an unknown fairness metric"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-5 (induced by source paper "adaptive discretization for episodic reinforcement learning in metric spaces"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "nonparametric contextual bandits in an unknown metric space"
    - "stochastic lipschitz q learning"
    - "learning to control in metric space with optimal regret"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"

Connected through hyperedge HE-6 (induced by source paper "learning to scaffold the development of robotic manipulation skills"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "deep reinforcement learning for high precision assembly tasks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-7 (induced by source paper "active learning for binary classification with abstention"):
  with members:
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "x armed bandits"

Connected through hyperedge HE-8 (induced by source paper "exploiting class learnability in noisy data"):
  with members:
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "automated curriculum learning for neural networks"
    - "learning what data to learn"
    - "learning to select data for transfer learning with bayesian optimization"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "yfcc100m the new data in multimedia research"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "training convolutional networks with noisy labels"

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.DS(Data Structures and Algorithms)