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: "fastrack a modular framework for fast and guaranteed safe motion planning"
  Abstract: Fast and safe navigation of dynamical systems through a priori unknown cluttered environments is vital to many applications of autonomous systems. However, trajectory planning for autonomous systems is computationally intensive, often requiring simplified dynamics that sacrifice safety and dynamic feasibility in order to plan efficiently. Conversely, safe trajectories can be computed using more sophisticated dynamic models, but this is typically too slow to be used for real-time planning. We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems. A path or trajectory planner using simplified dynamics to plan quickly can be incorporated into the FaSTrack framework, which provides a safety controller for the vehicle along with a guaranteed tracking error bound. This bound captures all possible deviations due to high dimensional dynamics and external disturbances. Note that FaSTrack is modular and can be used with most current path or trajectory planners. We demonstrate this framework using a 10D nonlinear quadrotor model tracking a 3D path obtained from an RRT planner.

Connected through hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty"):
  with members:
    - "control barrier functions theory and applications"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "algorithms for verifying deep neural networks"
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "worst cases policy gradients"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"

Connected through hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates"):
  with members:
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "algorithms for verifying deep neural networks"
    - "worst cases policy gradients"
    - "learning controls using cross modal representations bridging simulation and reality for drone racing"
    - "oil observational imitation learning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "reachable sets for safe real time manipulator trajectory design"):
  with members:
    - "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"
    - "safe aggressive quadrotor flight via reachability based trajectory design"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "funnel libraries for real time robust feedback motion planning"
    - "reducing the barrier to entry of complex robotic software a moveit case study"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "safe mission planning under dynamical uncertainties"):
  with members:
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "probabilistically safe vehicle control in a hostile environment"

Connected through hyperedge HE-5 (induced by source paper "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "safe and complete real time planning and exploration in unknown environments"
    - "a lyapunov based approach to safe reinforcement learning"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "constrained policy optimization"
    - "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"
    - "hamilton jacobi reachability a brief overview and recent advances"

Connected through hyperedge HE-6 (induced by source paper "verifiably safe off model reinforcement learning"):
  with members:
    - "logically correct reinforcement learning"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "shielded decision making in mdps"
    - "falsification of cyber physical systems using deep reinforcement learning"
    - "a complete uniform substitution calculus for differential dynamic logic"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-7 (induced by source paper "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"):
  with members:
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "identifying robust landmarks in feature based maps"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "on a formal model of safe and scalable self driving cars"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "funnel libraries for real time robust feedback motion planning"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"

Connected through hyperedge HE-8 (induced by source paper "a new simulation metric to determine safe environments and controllers for systems with unknown dynamics"):
  with members:
    - "soter programming safe robotics system using runtime assurance"
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "goal driven dynamics learning via bayesian optimization"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "learning quadrotor dynamics using neural network for flight control"
    - "diagnosis and repair for synthesis from signal temporal logic specifications"
    - "towards verified artificial intelligence"

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.RO(Robotics)