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 new simulation metric to determine safe environments and controllers for systems with unknown dynamics"):
  Abstract: We consider the problem of extracting safe environments and controllers for reach-avoid objectives for systems with known state and control spaces, but unknown dynamics. In a given environment, a common approach is to synthesize a controller from an abstraction or a model of the system (potentially learned from data). However, in many situations, the relationship between the dynamics of the model and the actual system is not known; and hence it is difficult to provide safety guarantees for the system. In such cases, the Standard Simulation Metric (SSM), defined as the worst-case norm distance between the model and the system output trajectories, can be used to modify a reach-avoid specification for the system into a more stringent specification for the abstraction. Nevertheless, the obtained distance, and hence the modified specification, can be quite conservative. This limits the set of environments for which a safe controller can be obtained. We propose SPEC, a specification-centric simulation metric, which overcomes these limitations by computing the distance using only the trajectories that violate the specification for the system. We show that modifying a reach-avoid specification with SPEC allows us to synthesize a safe controller for a larger set of environments compared to SSM. We also propose a probabilistic method to compute SPEC for a general class of systems. Case studies using simulators for quadrotors and autonomous cars illustrate the advantages of the proposed metric for determining safe environment sets and controllers.

Includes members:
  Member "soter programming safe robotics system using runtime assurance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "input validation for neural networks via runtime local robustness verification")
  Member "soter a runtime assurance framework for programming safe robotics systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "input validation for neural networks via runtime local robustness verification")
  Member "goal driven dynamics learning via bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the bottleneck simulator a model based deep reinforcement learning approach")
  Member "a general safety framework for learning based control in uncertain robotic systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "fastrack a modular framework for fast and guaranteed safe motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "learning quadrotor dynamics using neural network for flight control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "validation of image based neural network controllers through adaptive stress testing")
  Member "diagnosis and repair for synthesis from signal temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "metrics for signal temporal logic formulae")
  Member "towards verified artificial intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "teaser fast and certifiable point cloud registration")

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)