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: "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
  Abstract: In order to enable microaerial vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory-optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem or solves it by using an optimistic global planner. We present a conservative trajectory-optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.

Connected through hyperedge HE-1 (induced by source paper "an efficient sampling based method for online informative path planning in unknown environments"):
  with members:
    - "intel realsense stereoscopic depth cameras"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "fast frontier based information driven autonomous exploration with an mav"):
  with members:
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"

Connected through hyperedge HE-3 (induced by source paper "real time long range trajectory replanning for uavs in presence of dynamics obstacles"):
  with members:
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"

Connected through hyperedge HE-4 (induced by source paper "computationally efficient obstacle avoidance trajectory planner for uavs based on heuristic angular search method"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"

Connected through hyperedge HE-5 (induced by source paper "predicting unobserved space for planning via depth map augmentation"):
  with members:
    - "depth coefficients for depth completion"
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"

Connected through hyperedge HE-6 (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"
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "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-7 (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"
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "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-8 (induced by source paper "integrated motion planner for real time aerial videography with a drone in a dense environment"):
  with members:
    - "online trajectory generation of a mav for chasing a moving target in 3d dense environments"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "sampling based algorithms for optimal motion 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)