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: "bayesian optimization under heavy tailed payoffs"
  Abstract: We consider black box optimization of an unknown function in the nonparametric Gaussian process setting when the noise in the observed function values can be heavy tailed. This is in contrast to existing literature that typically assumes sub-Gaussian noise distributions for queries. Under the assumption that the unknown function belongs to the Reproducing Kernel Hilbert Space (RKHS) induced by a kernel, we first show that an adaptation of the well-known GP-UCB algorithm with reward truncation enjoys sublinear $\tilde{O}(T^{\frac{2 + \alpha}{2(1+\alpha)}})$ regret even with only the $(1+\alpha)$-th moments, $\alpha \in (0,1]$, of the reward distribution being bounded ($\tilde{O}$ hides logarithmic factors). However, for the common squared exponential (SE) and Matern kernels, this is seen to be significantly larger than a fundamental $\Omega(T^{\frac{1}{1+\alpha}})$ lower bound on regret. We resolve this gap by developing novel Bayesian optimization algorithms, based on kernel approximation techniques, with regret bounds matching the lower bound in order for the SE kernel. We numerically benchmark the algorithms on environments based on both synthetic models and real-world data sets.

Connected through hyperedge HE-1 (induced by source paper "one shot bayes opt with probabilistic population based training"):
  with members:
    - "a generalized framework for population based training"
    - "emergent coordination through competition"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "fast efficient hyperparameter tuning for policy gradients"
    - "massively parallel hyperparameter tuning"
    - "bayesian optimization in alphago"
    - "tune a research platform for distributed model selection and training"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-2 (induced by source paper "online learning in kernelized markov decision processes"):
  with members:
    - "learning unknown markov decision processes a thompson sampling approach"
    - "on kernelized multi armed bandits"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "online regret bounds for undiscounted continuous reinforcement learning"
    - "finite time analysis of kernelised contextual bandits"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

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)