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: "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"
  Abstract: Provably safe and scalable multi-vehicle path planning is an important and urgent problem due to the expected increase of automation in civilian airspace in the near future. Hamilton-Jacobi (HJ) reachability is an ideal tool for analyzing such safety-critical systems and has been successfully applied to several small-scale problems. However, a direct application of HJ reachability to large scale systems is often intractable because of its exponentially-scaling computation complexity with respect to system dimension, also known as the "curse of dimensionality". To overcome this problem, the sequential path planning (SPP) method, which assigns strict priorities to vehicles, was previously proposed; SPP allows multi-vehicle path planning to be done with a linearly-scaling computation complexity. In this work, we demonstrate the potential of SPP algorithm for large-scale systems. In particular, we simulate large-scale multi-vehicle systems in two different urban environments, a city environment and a multi-city environment, and use the SPP algorithm for trajectory planning. SPP is able to efficiently design collision-free trajectories in both environments despite the presence of disturbances in vehicles' dynamics. To ensure a safe transition of vehicles to their destinations, our method automatically allocates space-time reservations to vehicles while accounting for the magnitude of disturbances such as wind in a provably safe way. Our simulation results show an intuitive multi-lane structure in airspace, where the number of lanes and the distance between the lanes depend on the size of disturbances and other problem parameters.

Connected through hyperedge HE-1 (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"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "hamilton jacobi reachability a brief overview and recent advances"

Connected through hyperedge HE-2 (induced by source paper "modeling cyber physical human systems via an interplay between reinforcement learning and game theory"):
  with members:
    - "a game theoretical framework for the evaluation of unmanned aircraft systems airspace integration concepts"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "safe platooning of unmanned aerial vehicles via reachability"
    - "cyber physical security a game theory model of humans interacting over control systems"
    - "counter factual reinforcement learning how to model decision makers that anticipate the future"
    - "game theoretic modeling of pilot behavior during mid air encounters"

Connected through hyperedge HE-3 (induced by source paper "hamilton jacobi reachability a brief overview and recent advances"):
  with members:
    - "recursive regression with neural networks approximating the hji pde solution"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "funnel libraries for real time robust feedback motion planning"
    - "using neural networks to compute approximate and guaranteed feasible hamilton jacobi bellman pde solutions"
    - "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming"
    - "safe sequential path planning of multi vehicle systems under presence of disturbances and imperfect information"

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.SY(Systems and Control)