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 "faster fast and safe trajectory planner for flights in unknown environments"):
  Abstract: Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approach to ensure safety is to enforce a "stop" condition in the free-known space. However, this can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety guarantees are ensured by always having a feasible, safe back-up trajectory in the free-known space at the start of each replanning step. The Mixed Integer Quadratic Program formulation proposed allows the solver to choose the trajectory interval allocation, and the time allocation is found by a line search algorithm initialized with a heuristic computed from the previous replanning iteration. This proposed algorithm is tested extensively both in simulation and in real hardware, showing agile flights in unknown cluttered environments with velocities up to 7.8 m/s. To demonstrate the generality of the proposed framework, FASTER is also applied to a skid-steer robot, and the maximum speed specified for the robot (2 m/s) is achieved in real hardware experiments.

Includes members:
  Member "robust and efficient quadrotor trajectory generation for fast autonomous flight":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "rapid collision detection for multicopter trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rectangular pyramid partitioning using integrated depth sensors rappids a fast planner for multicopter navigation")
  Member "fast and agile vision based flight with teleoperation and collision avoidance on a multirotor":
    also connected through other hyperedges:
      (none)
  Member "real time planning with multi fidelity models for agile flights in unknown environments":
    also connected through other hyperedges:
      (none)
  Member "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust vision based obstacle avoidance for micro aerial vehicles in dynamic environments")
  Member "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an efficient sampling based method for online informative path planning in unknown environments")
  Member "search based motion planning for aggressive flight in se 3":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an efficient b spline based kinodynamic replanning framework for quadrotors")
  Member "search based motion planning for quadrotors using linear quadratic minimum time control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "autonomous navigation in unknown environments using sparse kernel based occupancy mapping")

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)