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: "scalable synthesis of safety certificates from data with application to learning based control"
  Abstract: The control of complex systems faces a trade-off between high performance and safety guarantees, which in particular restricts the application of learning-based methods to safety-critical systems. A recently proposed framework to address this issue is the use of a safety controller, which guarantees to keep the system within a safe region of the state space. This paper introduces efficient techniques for the synthesis of a safe set and control law, which offer improved scalability properties by relying on approximations based on convex optimization problems. The first proposed method requires only an approximate linear system model and Lipschitz continuity of the unknown nonlinear dynamics. The second method extends the results by showing how a Gaussian process prior on the unknown system dynamics can be used in order to reduce conservatism of the resulting safe set. We demonstrate the results with numerical examples, including an autonomous convoy of vehicles.

Connected through hyperedge HE-1 (induced by source paper "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"):
  with members:
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "a lyapunov based approach to safe reinforcement learning"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "safe reinforcement learning via shielding"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "safe exploration of nonlinear dynamical systems a predictive safety filter for reinforcement learning"):
  with members:
    - "linear model predictive safety certification for learning based control"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "learning to adapt meta learning for model based control"
    - "cautious model predictive control using gaussian process regression"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "constrained policy optimization"

Connected through hyperedge HE-3 (induced by source paper "linear model predictive safety certification for learning based control"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "learning human behaviors from motion capture by adversarial imitation"

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)