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 "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"):
  Abstract: As autonomous robots increasingly become part of daily life, they will often encounter dynamic environments while only having limited information about their surroundings. Unfortunately, due to the possible presence of malicious dynamic actors, it is infeasible to develop an algorithm that can guarantee collision-free operation. Instead, one can attempt to design a control technique that guarantees the robot is not-at-fault in any collision. In the literature, making such guarantees in real time has been restricted to static environments or specific dynamic models. To ensure not-at-fault behavior, a robot must first correctly sense and predict the world around it within some sufficiently large sensor horizon (the prediction problem), then correctly control relative to the predictions (the control problem). This paper addresses the control problem by proposing Reachability-based Trajectory Design for Dynamic environments (RTD-D), which guarantees that a robot with an arbitrary nonlinear dynamic model correctly responds to predictions in arbitrary dynamic environments. RTD-D first computes a Forward Reachable Set (FRS) offline of the robot tracking parameterized desired trajectories that include fail-safe maneuvers. Then, for online receding-horizon planning, the method provides a way to discretize predictions of an arbitrary dynamic environment to enable real-time collision checking. The FRS is used to map these discretized predictions to trajectories that the robot can track while provably not-at-fault. One such trajectory is chosen at each iteration, or the robot executes the fail-safe maneuver from its previous trajectory which is guaranteed to be not at fault. RTD-D is shown to produce not-at-fault behavior over thousands of simulations and several real-world hardware demonstrations on two robots: a Segway, and a small electric vehicle.

Includes members:
  Member "occlusion aware risk assessment for autonomous driving in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tackling existence probabilities of objects with motion planning for automated urban driving")
  Member "identifying robust landmarks in feature based maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large scale real time visual inertial localization revisited")
  Member "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reachable sets for safe real time manipulator trajectory design")
  Member "a scalable framework for real time multi robot multi human collision avoidance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference based strategy alignment for general sum differential games")
  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 "on a formal model of safe and scalable self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies")
  Member "safe trajectory synthesis for autonomous driving in unforeseen environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highly parallelized data driven mpc for minimal intervention shared control")
  Member "funnel libraries for real time robust feedback motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probably approximately correct vision based planning using motion primitives")

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)