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 "safety considerations in deep control policies with safety barrier certificates under uncertainty"):
  Abstract: Recent advances in Deep Machine Learning have shown promise in solving complex perception and control loops via methods such as reinforcement and imitation learning. However, guaranteeing safety for such learned deep policies has been a challenge due to issues such as partial observability and difficulties in characterizing the behavior of the neural networks. While a lot of emphasis in safe learning has been placed during training, it is non-trivial to guarantee safety at deployment or test time. This paper extends how under mild assumptions, Safety Barrier Certificates can be used to guarantee safety with deep control policies despite uncertainty arising due to perception and other latent variables. Specifically for scenarios where the dynamics are smooth and uncertainty has a finite support, the proposed framework wraps around an existing deep control policy and generates safe actions by dynamically evaluating and modifying the policy from the embedded network. Our framework utilizes control barrier functions to create spaces of control actions that are safe under uncertainty, and when the original actions are found to be in violation of the safety constraint, uses quadratic programming to minimally modify the original actions to ensure they lie in the safe set. Representations of the environment are built through Euclidean signed distance fields that are then used to infer the safety of actions and to guarantee forward invariance. We implement this method in simulation in a drone-racing environment and show that our method results in safer actions compared to a baseline that only relies on imitation learning to generate control actions.

Includes members:
  Member "control barrier functions theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network")
  Member "safe reinforcement learning with scene decomposition for navigating complex urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates")
  Member "barrier certified adaptive reinforcement learning with applications to brushbot navigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions")
  Member "algorithms for verifying deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parot a practical framework for robust deep neural network training")
  Member "an efficient reachability based framework for provably safe autonomous navigation in unknown environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe mission planning under dynamical uncertainties")
  Member "temporal logic guided safe reinforcement learning using control barrier functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "worst cases policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates")
  Member "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploration exploitation in constrained mdps")

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.RO(Robotics)