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 "a general safety framework for learning based control in uncertain robotic systems"):
  Abstract: The proven efficacy of learning-based control schemes strongly motivates their application to robotic systems operating in the physical world. However, guaranteeing correct operation during the learning process is currently an unresolved issue, which is of vital importance in safety-critical systems. We propose a general safety framework based on Hamilton-Jacobi reachability methods that can work in conjunction with an arbitrary learning algorithm. The method exploits approximate knowledge of the system dynamics to guarantee constraint satisfaction while minimally interfering with the learning process. We further introduce a Bayesian mechanism that refines the safety analysis as the system acquires new evidence, reducing initial conservativeness when appropriate while strengthening guarantees through real-time validation. The result is a least-restrictive, safety-preserving control law that intervenes only when (a) the computed safety guarantees require it, or (b) confidence in the computed guarantees decays in light of new observations. We prove theoretical safety guarantees combining probabilistic and worst-case analysis and demonstrate the proposed framework experimentally on a quadrotor vehicle. Even though safety analysis is based on a simple point-mass model, the quadrotor successfully arrives at a suitable controller by policy-gradient reinforcement learning without ever crashing, and safely retracts away from a strong external disturbance introduced during flight.

Includes members:
  Member "adversarial attacks on neural network policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "fastrack a modular framework for fast and guaranteed safe motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "transfer from simulation to real world through learning deep inverse dynamics model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems")
  Member "safe sequential path planning of multi vehicle systems via double obstacle hamilton jacobi isaacs variational inequality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "a modified riccati transformation for decentralized computation of the viability kernel under lti dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hamilton jacobi reachability a brief overview and recent advances")
  Member "safe exploration in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "risk sensitive reinforcement learning applied to control under constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma")

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)