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: "stagewise safe bayesian optimization with gaussian processes"
  Abstract: Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robotic control. We consider this problem of optimizing an unknown utility function with absolute feedback or preference feedback subject to unknown safety constraints. We develop an efficient safe Bayesian optimization algorithm, StageOpt, that separates safe region expansion and utility function maximization into two distinct stages. Compared to existing approaches which interleave between expansion and optimization, we show that StageOpt is more efficient and naturally applicable to a broader class of problems. We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value. We evaluate StageOpt on both a variety of synthetic experiments, as well as in clinical practice. We demonstrate that StageOpt is more effective than existing safe optimization approaches, and is able to safely and effectively optimize spinal cord stimulation therapy in our clinical experiments.

Connected through hyperedge HE-1 (induced by source paper "safe linear thompson sampling with side information"):
  with members:
    - "linear stochastic bandits under safety constraints"
    - "learning based model predictive control for safe exploration"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "control regularization for reduced variance reinforcement learning"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "neural lander stable drone landing control using learned dynamics"
    - "spectral normalization for generative adversarial networks"
    - "measuring and regularizing networks in function space"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-3 (induced by source paper "linear stochastic bandits under safety constraints"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "constrained policy optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "linear contextual bandits with knapsacks"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-4 (induced by source paper "a control lyapunov perspective on episodic learning via projection to state stability"):
  with members:
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "neural lander stable drone landing control using learned dynamics"
    - "differentiable mpc for end to end planning and control"
    - "spectral normalization for generative adversarial networks"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "torque saturation in bipedal robotic walking through control lyapunov function based quadratic programs"

Connected through hyperedge HE-5 (induced by source paper "safe exploration for interactive machine learning"):
  with members:
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "efficient and safe exploration in deterministic markov decision processes with unknown transition models"
    - "on kernelized multi armed bandits"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-6 (induced by source paper "safe linear thompson sampling"):
  with members:
    - "linear stochastic bandits under safety constraints"
    - "learning based model predictive control for safe exploration"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "probabilistic line searches for stochastic optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "dueling posterior sampling for preference based reinforcement learning"):
  with members:
    - "multi dueling bandits with dependent arms"
    - "learning dynamic robot to human object handover from human feedback"
    - "concrete problems in ai safety"
    - "contextual dueling bandits"

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)