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: "robust and efficient quadrotor trajectory generation for fast autonomous flight"
  Abstract: In this paper, we propose a robust and efficient quadrotor motion planning system for fast flight in 3-D complex environments. We adopt a kinodynamic path searching method to find a safe, kinodynamic feasible and minimum-time initial trajectory in the discretized control space. We improve the smoothness and clearance of the trajectory by a B-spline optimization, which incorporates gradient information from a Euclidean distance field (EDF) and dynamic constraints efficiently utilizing the convex hull property of B-spline. Finally, by representing the final trajectory as a non-uniform B-spline, an iterative time adjustment method is adopted to guarantee dynamically feasible and non-conservative trajectories. We validate our proposed method in various complex simulational environments. The competence of the method is also validated in challenging real-world tasks. We release our code as an open-source package.

Connected through hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "neuroflight next generation flight control firmware"
    - "real time reinforcement learning"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-2 (induced by source paper "region of interest segmentation from lidar point cloud for multirotor aerial vehicles"):
  with members:
    - "ground profile recovery from aerial 3d lidar based maps"
    - "near real time data labeling using a depth sensor for emg based prosthetic arms"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "high resolution lidar based depth mapping using bilateral filter"

Connected through hyperedge HE-3 (induced by source paper "computationally efficient obstacle avoidance trajectory planner for uavs based on heuristic angular search method"):
  with members:
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"

Connected through hyperedge HE-4 (induced by source paper "teach repeat replan a complete and robust system for aggressive flight in complex environments"):
  with members:
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "real time scalable dense surfel mapping"
    - "visual inertial teach and repeat for aerial inspection"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "a new approach to time optimal path parameterization based on reachability analysis"
    - "topomap topological mapping and navigation based on visual slam maps"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

Connected through hyperedge HE-5 (induced by source paper "faster fast and safe trajectory planner for flights in unknown environments"):
  with members:
    - "rapid collision detection for multicopter trajectories"
    - "fast and agile vision based flight with teleoperation and collision avoidance on a multirotor"
    - "real time planning with multi fidelity models for agile flights in unknown environments"
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "search based motion planning for aggressive flight in se 3"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-6 (induced by source paper "robust real time uav replanning using guided gradient based optimization and topological paths"):
  with members:
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "trajectory replanning for quadrotors using kinodynamic search and elastic optimization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "sparse 3d topological graphs for micro aerial vehicle planning"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "topomap topological mapping and navigation based on visual slam maps"

Connected through hyperedge HE-7 (induced by source paper "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"):
  with members:
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "sparse 3d topological graphs for micro aerial vehicle planning"
    - "topomap topological mapping and navigation based on visual slam maps"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

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.RO(Robotics)