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 "teach repeat replan a complete and robust system for aggressive flight in complex environments"):
  Abstract: In this paper, we propose a complete and robust motion planning system for the aggressive flight of autonomous quadrotors. The proposed method is built upon on a classical teach-and-repeat framework, which is widely adopted in infrastructure inspection, aerial transportation, and search-and-rescue. For these applications, human's intention is essential to decide the topological structure of the flight trajectory of the drone. However, poor teaching trajectories and changing environments prevent a simple teach-and-repeat system from being applied flexibly and robustly. In this paper, instead of commanding the drone to precisely follow a teaching trajectory, we propose a method to automatically convert a human-piloted trajectory, which can be arbitrarily jerky, to a topologically equivalent one. The generated trajectory is guaranteed to be smooth, safe, and kinodynamically feasible, with a human preferable aggressiveness. Also, to avoid unmapped or dynamic obstacles during flights, a sliding-windowed local perception and re-planning method are introduced to our system, to generate safe local trajectories onboard. We name our system as teach-repeat-replan. It can capture users' intention of a flight mission, convert an arbitrarily jerky teaching path to a smooth repeating trajectory, and generate safe local re-plans to avoid unmapped or moving obstacles. The proposed planning system is integrated into a complete autonomous quadrotor with global and local perception and localization sub-modules. Our system is validated by performing aggressive flights in challenging indoor/outdoor environments. We release all components in our quadrotor system as open-source ros-packages.

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 "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "real time scalable dense surfel mapping":
    also connected through other hyperedges:
      (none)
  Member "visual inertial teach and repeat for aerial inspection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a mobile manipulation system for one shot teaching of complex tasks in homes")
  Member "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy")
  Member "vins mono a robust and versatile monocular visual inertial state estimator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "a new approach to time optimal path parameterization based on reachability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time optimal path tracking for industrial robots a dynamic model free reinforcement learning approach")
  Member "topomap topological mapping and navigation based on visual slam maps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy")

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)