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 bandits with similarity information"
  Abstract: In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a time-invariant set of alternatives and receives the payoff associated with this alternative. While the case of small strategy sets is by now well-understood, a lot of recent work has focused on MAB problems with exponentially or infinitely large strategy sets, where one needs to assume extra structure in order to make the problem tractable. In particular, recent literature considered information on similarity between arms. #R##N#We consider similarity information in the setting of "contextual bandits", a natural extension of the basic MAB problem where before each round an algorithm is given the "context" -- a hint about the payoffs in this round. Contextual bandits are directly motivated by placing advertisements on webpages, one of the crucial problems in sponsored search. A particularly simple way to represent similarity information in the contextual bandit setting is via a "similarity distance" between the context-arm pairs which gives an upper bound on the difference between the respective expected payoffs. #R##N#Prior work on contextual bandits with similarity uses "uniform" partitions of the similarity space, which is potentially wasteful. We design more efficient algorithms that are based on adaptive partitions adjusted to "popular" context and "high-payoff" arms.

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 "multi armed bandits for decentralized ap selection in enterprise wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "introduction to multi armed bandits"
    - "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams"
    - "a tutorial on thompson sampling"
    - "collaborative spatial reuse in wireless networks via selfish multi armed bandits"
    - "ieee 802 11ax high efficiency wlans"
    - "decentralized ap selection in large scale wireless lans considering multi ap interference"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"

Connected through hyperedge HE-3 (induced by source paper "nonparametric pricing analytics with customer covariates"):
  with members:
    - "a primal dual learning algorithm for personalized dynamic pricing with an inventory constraint"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "algorithms for multi armed bandit problems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "zooming for efficient model free reinforcement learning in metric spaces"):
  with members:
    - "learning to control in metric space with optimal regret"
    - "lipschitz continuity in model based reinforcement learning"
    - "is q learning provably efficient"
    - "online regret bounds for undiscounted continuous reinforcement learning"
    - "the sample complexity of general reinforcement learning"
    - "metrics for finite markov decision processes"

Connected through hyperedge HE-5 (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"
    - "bandits and experts in metric spaces"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-6 (induced by source paper "fair contextual multi armed bandits theory and experiments"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "reinforcement learning with fairness constraints for resource distribution in human robot teams"
    - "combinatorial sleeping bandits with fairness constraints"
    - "learning contextual bandits in a non stationary environment"
    - "robot assisted tower construction a resource distribution task to study human robot collaboration and interaction with groups of people"
    - "calibrated fairness in bandits"
    - "fairness in learning classic and contextual bandits"
    - "fair algorithms for infinite and contextual bandits"

Connected through hyperedge HE-7 (induced by source paper "stochastic lipschitz q learning"):
  with members:
    - "is q learning provably efficient"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "deep visual foresight for planning robot motion"
    - "asynchronous methods for deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"

Connected through hyperedge HE-8 (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"

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)