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 with safety constraints safe and automatic parameter tuning in robotics"
  Abstract: Robotic algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance. Optimization algorithms, such as Bayesian optimization, have been used to automate this process. However, these methods may evaluate unsafe parameters during the optimization process that lead to safety-critical system failures. Recently, a safe Bayesian optimization algorithm, called SafeOpt, has been developed, which guarantees that the performance of the system never falls below a critical value; that is, safety is defined based on the performance function. However, coupling performance and safety is often not desirable in robotics. For example, high-gain controllers might achieve low average tracking error (performance), but can overshoot and violate input constraints. In this paper, we present a generalized algorithm that allows for multiple safety constraints separate from the objective. Given an initial set of safe parameters, the algorithm maximizes performance but only evaluates parameters that satisfy safety for all constraints with high probability. To this end, it carefully explores the parameter space by exploiting regularity assumptions in terms of a Gaussian process prior. Moreover, we show how context variables can be used to safely transfer knowledge to new situations and tasks. We provide a theoretical analysis and demonstrate that the proposed algorithm enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.

Connected through hyperedge HE-1 (induced by source paper "probabilistic safety constraints for learned high relative degree system dynamics"):
  with members:
    - "control barrier functions theory and applications"
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "self triggered control for safety critical systems using control barrier functions"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "meta learning priors for efficient online bayesian regression"
    - "safe learning of quadrotor dynamics using barrier certificates"

Connected through hyperedge HE-2 (induced by source paper "linear stochastic bandits under safety constraints"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "constrained policy optimization"
    - "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-3 (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"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "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"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "posterior variance analysis of gaussian processes with application to average learning curves"):
  with members:
    - "gaussian process based passivation of a class of nonlinear systems with unknown dynamics"
    - "provably robust learning based approach for high accuracy tracking control of lagrangian systems"
    - "learning based model predictive control for safe exploration"
    - "on kernelized multi armed bandits"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-5 (induced by source paper "region of attraction for power systems using gaussian process and converse lyapunov function part i theoretical framework and off line study"):
  with members:
    - "high fidelity model order reduction for microgrids stability assessment"
    - "lyapunov functions family approach to transient stability assessment"
    - "probabilistic power flow computation via low rank and sparse tensor recovery"
    - "robust stability assessment in the presence of load dynamics uncertainty"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "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-7 (induced by source paper "efficient and safe exploration in deterministic markov decision processes with unknown transition models"):
  with members:
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "safe exploration of state and action spaces in reinforcement learning"
    - "safe exploration in markov decision processes"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-8 (induced by source paper "safe contextual bayesian optimization for sustainable room temperature pid control tuning"):
  with members:
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"

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.RO(Robotics)