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 "robust tracking with model mismatch for fast and safe planning an sos optimization approach"):
  Abstract: In the pursuit of real-time motion planning, a commonly adopted practice is to compute a trajectory by running a planning algorithm on a simplified, low-dimensional dynamical model, and then employ a feedback tracking controller that tracks such a trajectory by accounting for the full, high-dimensional system dynamics. While this strategy of planning with model mismatch generally yields fast computation times, there are no guarantees of dynamic feasibility, which hampers application to safety-critical systems. Building upon recent work that addressed this problem through the lens of Hamilton-Jacobi (HJ) reachability, we devise an algorithmic framework whereby one computes, offline, for a pair of "planner" (i.e., low-dimensional) and "tracking" (i.e., high-dimensional) models, a feedback tracking controller and associated tracking bound. This bound is then used as a safety margin when generating motion plans via the low-dimensional model. Specifically, we harness the computational tool of sum-of-squares (SOS) programming to design a bilinear optimization algorithm for the computation of the feedback tracking controller and associated tracking bound. The algorithm is demonstrated via numerical experiments, with an emphasis on investigating the trade-off between the increased computational scalability afforded by SOS and its intrinsic conservativeness. Collectively, our results enable scaling the appealing strategy of planning with model mismatch to systems that are beyond the reach of HJ analysis, while maintaining safety guarantees.

Includes members:
  Member "fastrack a modular framework for fast and guaranteed safe motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "safe trajectory synthesis for autonomous driving in unforeseen environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards provably not at fault control of autonomous robots in arbitrary dynamic environments")
  Member "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe speed control and collision probability estimation under ego pose uncertainty for autonomous vehicle")
  Member "funnel libraries for real time robust feedback motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "asymptotically optimal sampling based kinodynamic planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "experimental comparison of global motion planning algorithms for wheeled mobile robots")
  Member "optimal sampling based motion planning under differential constraints the driftless case":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "control design along trajectories with sums of squares programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local policy optimization for trajectory centric reinforcement learning")

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.SY(Systems and Control)