human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  Abstract: This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates that constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under  stationarity  assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex, and nonstationary.

Includes members:
  Member "safe learning of quadrotor dynamics using barrier certificates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi sparse gaussian process learning based semi parametric control")
  Member "constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "the robotarium a remotely accessible swarm robotics research testbed":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what can robotics research learn from computer vision research")
  Member "safe multi agent reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems")
  Member "safe policy search for lifelong reinforcement learning with sublinear regret":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compositional adam an adaptive compositional solver")
  Member "adaptive learning in cartesian product of reproducing kernel hilbert spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detection for 5g noma an online adaptive machine learning approach")
  Member "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")

Question: Please tell me which class the source paper 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)