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: "trajectory replanning for quadrotors using kinodynamic search and elastic optimization"
  Abstract: We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A * and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard.

Connected through hyperedge HE-1 (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"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "search based motion planning for aggressive flight in se 3"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "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-2 (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"
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "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-3 (induced by source paper "robust and efficient quadrotor trajectory generation for fast autonomous flight"):
  with members:
    - "an efficient b spline based kinodynamic replanning framework for quadrotors"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

Connected through hyperedge HE-4 (induced by source paper "real time trajectory generation for quadrotors using b spline based non uniform kinodynamic search"):
  with members:
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "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)