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: "exponential regret bounds for gaussian process bandits with deterministic observations"
  Abstract: This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al, 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, Srinivas et al proved that the regret vanishes at the approximate rate of $O(1/\sqrt{t})$, where t is the number of observations. To complement their result, we attack the deterministic case and attain a much faster exponential convergence rate. Under some regularity assumptions, we show that the regret decreases asymptotically according to $O(e^{-\frac{\tau t}{(\ln t)^{d/4}}})$ with high probability. Here, d is the dimension of the search space and tau is a constant that depends on the behaviour of the objective function near its global maximum.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "lipschitz bandit optimization with improved efficiency"):
  with members:
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "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"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "a simple parameter free and adaptive approach to optimization under a minimal local smoothness assumption"):
  with members:
    - "bandit algorithms for tree search"
    - "follow the leader if you can hedge if you must"
    - "the best of both worlds stochastic and adversarial bandits"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-5 (induced by source paper "context specific validation of data driven models"):
  with members:
    - "learning quadrotor dynamics using neural network for flight control"
    - "x armed bandits"

Connected through hyperedge HE-6 (induced by source paper "on kernelized multi armed bandits"):
  with members:
    - "learning to optimize via posterior sampling"
    - "finite time analysis of kernelised contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "hybrid batch bayesian optimization"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "budgeted batch bayesian optimization with unknown batch sizes"):
  with members:
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "hybrid batch bayesian optimization"

Connected through hyperedge HE-8 (induced by source paper "bayesian optimization of hyper parameters in reservoir computing"):
  with members:
    - "scaling up echo state networks with multiple light scattering"
    - "an experimental analysis of the echo state network initialization using the particle swarm optimization"
    - "unitary evolution recurrent neural networks"
    - "optoelectronic reservoir computing"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)