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: "a convex optimization approach to smooth trajectories for motion planning with car like robots"
  Abstract: In the recent past, several sampling-based algorithms have been proposed to compute trajectories that are collision-free and dynamically-feasible. However, the outputs of such algorithms are notoriously jagged. In this paper, by focusing on robots with car-like dynamics, we present a fast and simple heuristic algorithm, named Convex Elastic Smoothing (CES) algorithm, for trajectory smoothing and speed optimization. The CES algorithm is inspired by earlier work on elastic band planning and iteratively performs shape and speed optimization. The key feature of the algorithm is that both optimization problems can be solved via convex programming, making CES particularly fast. A range of numerical experiments show that the CES algorithm returns high-quality solutions in a matter of a few hundreds of milliseconds and hence appears amenable to a real-time implementation.

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"
    - "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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "teach repeat replan a complete and robust system for aggressive flight in complex environments"):
  with members:
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "real time scalable dense surfel mapping"
    - "visual inertial teach and repeat for aerial inspection"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "a new approach to time optimal path parameterization based on reachability analysis"
    - "topomap topological mapping and navigation based on visual slam maps"

Connected through hyperedge HE-3 (induced by source paper "map predictive motion planning in unknown environments"):
  with members:
    - "conditional neural processes"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "generative image inpainting with contextual attention"
    - "cognitive mapping and planning for visual navigation"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-4 (induced by source paper "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"):
  with members:
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "search based optimal motion planning for automated driving"
    - "baidu apollo em motion planner"

Connected through hyperedge HE-5 (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"
    - "trajectory replanning for quadrotors using kinodynamic search and elastic optimization"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"

Connected through hyperedge HE-6 (induced by source paper "an nmpc approach using convex inner approximations for online motion planning with guaranteed collision freedom"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "nonlinear model predictive control for constrained output path following"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "trajectory replanning for quadrotors using kinodynamic search and elastic optimization"):
  with members:
    - "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"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"

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)