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: "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
  Abstract: We would like robots to be able to safely navigate at high speed, efficiently use local 3D information, and robustly plan motions that consider pose uncertainty of measurements in a local map structure. This is hard to do with previously existing mapping approaches, like occupancy grids, that are focused on incrementally fusing 3D data into a common world frame. In particular, both their fragile sensitivity to state estimation errors and computational cost can be limiting. We develop an alternative framework, NanoMap, which alleviates the need for global map fusion and enables a motion planner to efficiently query pose-uncertainty-aware local 3D geometric information. The key idea of NanoMap is to store a history of noisy relative pose transforms and search over a corresponding set of depth sensor measurements for the minimum-uncertainty view of a queried point in space. This approach affords a variety of capabilities not offered by traditional mapping techniques: (a) the pose uncertainty associated with 3D data can be incorporated in motion planning, (b) poses can be updated (i.e., from loop closures) with minimal computational effort, and (c) 3D data can be fused lazily for the purpose of planning. We provide an open-source implementation of NanoMap, and analyze its capabilities and computational efficiency in simulation experiments. Finally, we demonstrate in hardware its effectiveness for fast 3D obstacle avoidance onboard a quadrotor flying up to 10 m/s.

Connected through hyperedge HE-1 (induced by source paper "robust vision based obstacle avoidance for micro aerial vehicles in dynamic environments"):
  with members:
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-2 (induced by source paper "rectangular pyramid partitioning using integrated depth sensors rappids a fast planner for multicopter navigation"):
  with members:
    - "rapid collision detection for multicopter trajectories"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "autonomous navigation of mavs in unknown cluttered environments"):
  with members:
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-4 (induced by source paper "faster fast and safe trajectory planner for flights in unknown environments"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "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"
    - "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-5 (induced by source paper "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "fast and agile vision based flight with teleoperation and collision avoidance on a multirotor"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "vision and learning for deliberative monocular cluttered flight"

Connected through hyperedge HE-7 (induced by source paper "ultra low power deep learning powered autonomous nano drones"):
  with members:
    - "build your own visual inertial drone a cost effective and open source autonomous drone"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "plato policy learning using adaptive trajectory optimization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "real time planning with multi fidelity models for agile flights in unknown environments"):
  with members:
    - "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"
    - "vision and learning for deliberative monocular cluttered flight"

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)