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: "information theoretic regret bounds for gaussian process optimization in the bandit setting"
  Abstract: Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low norm in a reproducing kernel Hilbert space. We resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization. We analyze an intuitive Gaussian process upper confidence bound (GP-UCB) algorithm, and bound its cumulative regret in terms of maximal in- formation gain, establishing a novel connection between GP optimization and experimental design. Moreover, by bounding the latter in terms of operator spectra, we obtain explicit sublinear regret bounds for many commonly used covariance functions. In some important cases, our bounds have surprisingly weak dependence on the dimensionality. In our experiments on real sensor data, GP-UCB compares favorably with other heuristical GP optimization approaches.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on hyper parameter tuning for stochastic optimization algorithms"):
  with members:
    - "on first order meta learning algorithms"
    - "hyperadam a learnable task adaptive adam for network training"
    - "meta learning with differentiable closed form solvers"
    - "deep reinforcement learning a brief survey"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "multiscale analysis for higher order tensors"
    - "generating long term trajectories using deep hierarchical networks"
    - "tensor regression meets gaussian processes"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "tensorly tensor learning in python"
    - "why should i trust you explaining the predictions of any classifier"
    - "parallel tensor compression for large scale scientific data"

Connected through hyperedge HE-4 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "on kernelized multi armed bandits"
    - "context aware bandits"
    - "a gang of bandits"
    - "sparse gaussian processes for multi task learning"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-5 (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"
    - "bandits and experts in metric spaces"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"

Connected through hyperedge HE-6 (induced by source paper "bayesian optimisation for safe navigation under localisation uncertainty"):
  with members:
    - "unscented bayesian optimization for safe robot grasping"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "tight regret bounds for noisy optimization of a brownian motion"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "information based complexity feedback and dynamics in convex programming"

Connected through hyperedge HE-8 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

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)