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: "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
  Abstract: Autonomous mobile robots must operate with limited sensor horizons in unpredictable environments. To do so, they use a receding-horizon strategy to plan trajectories, by executing a short plan while creating the next plan. However, creating safe, dynamically-feasible trajectories in real time is challenging; and, planners must ensure that they are persistently feasible, meaning that a new trajectory is always available before the previous one has finished executing. Existing approaches make a tradeoff between model complexity and planning speed, which can require sacrificing guarantees of safety and dynamic feasibility. This work presents the Reachability-based Trajectory Design (RTD) method for trajectory planning. RTD begins with an offline Forward Reachable Set (FRS) computation of a robot's motion while it tracks parameterized trajectories; the FRS also provably bounds tracking error. At runtime, the FRS is used to map obstacles to the space of parameterized trajectories, which allows RTD to select a safe trajectory at every planning iteration. RTD prescribes a method of representing obstacles to ensure that these constraints can be created and evaluated in real time while maintaining provable safety. Persistent feasibility is achieved by prescribing a minimum duration of planned trajectories, and a minimum sensor horizon. A system decomposition approach is used to increase the dimension of the parameterized trajectories in the FRS, allowing for RTD to create more complex plans at runtime. RTD is compared in simulation with Rapidly-exploring Random Trees (RRT) and Nonlinear Model-Predictive Control (NMPC). RTD is also demonstrated on two hardware platforms in randomly-crafted environments: a differential-drive Segway, and a car-like Rover. The proposed method is shown as safe and persistently feasible across thousands of simulations and dozens of hardware demos.

Connected through hyperedge HE-1 (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"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "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-2 (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"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "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-3 (induced by source paper "highly parallelized data driven mpc for minimal intervention shared control"):
  with members:
    - "operation and imitation under safety aware shared control"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "on infusing reachability based safety assurance within probabilistic planning frameworks for human robot vehicle interactions"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "model based control using koopman operators"

Connected through hyperedge HE-4 (induced by source paper "technical report safe aggressive quadrotor flight via reachability based trajectory design"):
  with members:
    - "guaranteed safe reachability based trajectory design for a high fidelity model of an autonomous passenger vehicle"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"

Connected through hyperedge HE-5 (induced by source paper "leveraging the template and anchor framework for safe online robotic gait design"):
  with members:
    - "walking with confidence safety regulation for full order biped models"
    - "safe aggressive quadrotor flight via reachability based trajectory design"
    - "safe adaptive switching among dynamical movement primitives application to 3d limit cycle walkers"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-6 (induced by source paper "guaranteed safe reachability based trajectory design for a high fidelity model of an autonomous passenger vehicle"):
  with members:
    - "robust tracking with model mismatch for fast and safe planning an sos optimization approach"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "funnel libraries for real time robust feedback motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "safe aggressive quadrotor flight via reachability based trajectory design"):
  with members:
    - "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"
    - "guaranteed safe reachability based trajectory design for a high fidelity model of an autonomous passenger vehicle"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"

Connected through hyperedge HE-8 (induced by source paper "reachability based safety guarantees using efficient initializations"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "robust sequential path planning under disturbances and adversarial intruder"
    - "synthesis of separable controlled invariant sets for modular local control design"

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)