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: "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
  Abstract: Control theory can provide useful insights into the properties of controlled, dynamic systems. One important property of nonlinear systems is the region of attraction (ROA), a safe subset of the state space in which a given controller renders an equilibrium point asymptotically stable. The ROA is typically estimated based on a model of the system. However, since models are only an approximation of the real world, the resulting estimated safe region can contain states outside the ROA of the real system. This is not acceptable in safety-critical applications. In this paper, we consider an approach that learns the ROA from experiments on a real system, without ever leaving the true ROA and, thus, without risking safety-critical failures. Based on regularity assumptions on the model errors in terms of a Gaussian process prior, we use an underlying Lyapunov function in order to determine a region in which an equilibrium point is asymptotically stable with high probability. Moreover, we provide an algorithm to actively and safely explore the state space in order to expand the ROA estimate. We demonstrate the effectiveness of this method in simulation.

Connected through hyperedge HE-1 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems"):
  with members:
    - "stable gaussian process based tracking control of euler lagrange systems"
    - "a control lyapunov perspective on episodic learning via projection to state stability"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "neural lander stable drone landing control using learned dynamics"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "sim to real learning agile locomotion for quadruped robots"
    - "a lyapunov based approach to safe reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-2 (induced by source paper "safety guided deep reinforcement learning via online gaussian process estimation"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "ai safety gridworlds"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "constrained policy optimization"
    - "on kernelized multi armed bandits"
    - "deep q learning from demonstrations"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-3 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  with members:
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "constrained policy optimization"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "adaptive learning in cartesian product of reproducing kernel hilbert spaces"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "practical kernel based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "control theory meets pomdps a hybrid systems approach"):
  with members:
    - "safe policy synthesis in multi agent pomdps via discrete time barrier functions"
    - "verification of uncertain pomdps using barrier certificates"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "barrier certificates for assured machine teaching"
    - "privacy verification in pomdps via barrier certificates"
    - "machine teaching a new paradigm for building machine learning systems"
    - "heuristic search value iteration for pomdps"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"

Connected through hyperedge HE-5 (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"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "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"
    - "torque saturation in bipedal robotic walking through control lyapunov function based quadratic programs"

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"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "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 "uniform error bounds for gaussian process regression with application to safe control"):
  with members:
    - "posterior variance analysis of gaussian processes with application to average learning curves"
    - "stable gaussian process based tracking control of euler lagrange systems"
    - "on kernelized multi armed bandits"
    - "an empirical evaluation of deep learning on highway driving"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-8 (induced by source paper "stable gaussian process based tracking control of euler lagrange systems"):
  with members:
    - "mean square prediction error of misspecified gaussian process models"
    - "stable gaussian process based tracking control of lagrangian systems"
    - "equilibrium distributions and stability analysis of gaussian process state space models"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

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.SY(Systems and Control)