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: "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
  Abstract: In this paper, we present a real-time approach to local trajectory replanning for microaerial vehicles (MAVs). Current trajectory generation methods for multicopters achieve high success rates in cluttered environments, but assume that the environment is static and require prior knowledge of the map. In the presented study, we use the results of such planners and extend them with a local replanning algorithm that can handle unmodeled (possibly dynamic) obstacles while keeping the MAV close to the global trajectory. To ensure that the proposed approach is real-time capable, we maintain information about the environment around the MAV in an occupancy grid stored in a three-dimensional circular buffer, which moves together with a drone, and represent the trajectories by using uniform B-splines. This representation ensures that the trajectory is sufficiently smooth and simultaneously allows for efficient optimization.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "real time long range trajectory replanning for uavs in presence of dynamics obstacles"):
  with members:
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"

Connected through hyperedge HE-3 (induced by source paper "an efficient b spline based kinodynamic replanning framework for quadrotors"):
  with members:
    - "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"
    - "trajectory replanning for quadrotors using kinodynamic search and elastic optimization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "search based motion planning for aggressive flight in se 3"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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-5 (induced by source paper "walking posture adaptation for legged robot navigation in confined spaces"):
  with members:
    - "quotient space motion planning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "footstep and motion planning in semi unstructured environments using randomized possibility graphs"

Connected through hyperedge HE-6 (induced by source paper "efficient derivative computation for cumulative b splines on lie groups"):
  with members:
    - "continuous time visual inertial odometry for event cameras"
    - "trajectory representation and landmark projection for continuous time structure from motion"
    - "spline error weighting for robust visual inertial fusion"
    - "a micro lie theory for state estimation in robotics"

Connected through hyperedge HE-7 (induced by source paper "continuous time trajectory optimization for decentralized multi robot navigation"):
  with members:
    - "high speed trajectory planning for autonomous vehicles using a simple dynamic model"
    - "safe certificate based maneuvers for teams of quadrotors using differential flatness"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-8 (induced by source paper "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "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"
    - "sparse 3d topological graphs for micro aerial vehicle planning"
    - "c blox a scalable and consistent tsdf based dense mapping approach"
    - "nonlinear model predictive control for multi micro aerial vehicle robust collision avoidance"
    - "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)