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: "search based motion planning for quadrotors using linear quadratic minimum time control"
  Abstract: In this work, we propose a search-based planning method to compute dynamically feasible trajectories for a quadrotor flying in an obstacle-cluttered environment. Our approach searches for smooth, minimum-time trajectories by exploring the map using a set of short-duration motion primitives. The primitives are generated by solving an optimal control problem and induce a finite lattice discretization on the state space which can be explored using a graph-search algorithm. The proposed approach is able to generate resolution-complete (i.e., optimal in the discretized space), safe, dynamically feasibility trajectories efficiently by exploiting the explicit solution of a Linear Quadratic Minimum Time problem. It does not assume a hovering initial condition and, hence, is suitable for fast online re-planning while the robot is moving. Quadrotor navigation with online re-planning is demonstrated using the proposed approach in simulation and physical experiments and comparisons with trajectory generation based on state-of-art quadratic programming are presented.

Connected through hyperedge HE-1 (induced by source paper "autonomous navigation in unknown environments using sparse kernel based occupancy mapping"):
  with members:
    - "learning based proxy collision detection for robot motion planning applications"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "warped gaussian processes occupancy mapping with uncertain inputs"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-2 (induced by source paper "fast and safe path following control using a state dependent directional metric"):
  with members:
    - "funnel libraries for real time robust feedback motion planning"
    - "sampling based algorithms for optimal motion planning"

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"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "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 "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"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "search based motion planning for aggressive flight in se 3"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-5 (induced by source paper "large scale model predictive control with neural networks and primal active sets"):
  with members:
    - "safe and near optimal policy learning for model predictive control using primal dual neural networks"
    - "learning an approximate model predictive controller with guarantees"
    - "understanding deep neural networks with rectified linear units"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "embedded online optimization for model predictive control at megahertz rates"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "decentralized multi target tracking in urban environments overview and challenges"):
  with members:
    - "towards search based motion planning for micro aerial vehicles"
    - "sensor selection for target tracking in wireless sensor networks with uncertainty"
    - "the cauchy schwarz divergence for poisson point processes"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "cooperative multi target localization with noisy sensors"
    - "point based pomdp algorithms improved analysis and implementation"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-7 (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"
    - "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"
    - "topomap topological mapping and navigation based on visual slam maps"

Connected through hyperedge HE-8 (induced by source paper "inverse optimal planning for air traffic control"):
  with members:
    - "towards search based motion planning for micro aerial vehicles"
    - "search based motion planning for aggressive flight in se 3"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"

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)