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: "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.

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"
    - "neuroflight next generation flight control firmware"
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "real time reinforcement learning"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-2 (induced by source paper "alternating minimization based trajectory generation for quadrotor aggressive flight"):
  with members:
    - "detailed proofs of alternating minimization based trajectory generation for quadrotor aggressive flight"
    - "fast uav trajectory optimization using bilevel optimization with analytical gradients"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "a hybrid method for online trajectory planning of mobile robots in cluttered environments"
    - "computing real roots of real polynomials"
    - "sampling based algorithms for optimal motion planning"
    - "univariate polynomial real root isolation continued fractions revisited"

Connected through hyperedge HE-3 (induced by source paper "robust real time uav replanning using guided gradient based optimization and topological paths"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "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"

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)