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: "kinodynamic rrt optimal motion planning for systems with linear differential constraints"
  Abstract: We present Kinodynamic RRT*, an incremental sampling-based approach for asymptotically optimal motion planning for robots with linear differential constraints. Our approach extends RRT*, which was introduced for holonomic robots (Karaman et al. 2011), by using a fixed-final-state-free-final-time controller that exactly and optimally connects any pair of states, where the cost function is expressed as a trade-off between the duration of a trajectory and the expended control effort. Our approach generalizes earlier work on extending RRT* to kinodynamic systems, as it guarantees asymptotic optimality for any system with controllable linear dynamics, in state spaces of any dimension. Our approach can be applied to non-linear dynamics as well by using their first-order Taylor approximations. In addition, we show that for the rich subclass of systems with a nilpotent dynamics matrix, closed-form solutions for optimal trajectories can be derived, which keeps the computational overhead of our algorithm compared to traditional RRT* at a minimum. We demonstrate the potential of our approach by computing asymptotically optimal trajectories in three challenging motion planning scenarios: (i) a planar robot with a 4-D state space and double integrator dynamics, (ii) an aerial vehicle with a 10-D state space and linearized quadrotor dynamics, and (iii) a car-like robot with a 5-D state space and non-linear dynamics.

Connected through hyperedge HE-1 (induced by source paper "revisiting the asymptotic optimality of rrt"):
  with members:
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "the critical radius in sampling based motion planning"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "incremental sampling based algorithm for minimum violation motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "data driven policy transfer with imprecise perception simulation"):
  with members:
    - "fast simulation of vehicles with non deformable tracks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "segmatch segment based place recognition in 3d point clouds"
    - "learning from simulated and unsupervised images through adversarial training"
    - "transfer from simulation to real world through learning deep inverse dynamics model"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "fast motion planning for high dof robot systems using hierarchical system identification"):
  with members:
    - "deep haptic model predictive control for robot assisted dressing"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "learning to navigate cloth using haptics"
    - "proximal policy optimization algorithms"
    - "two hierarchies of spline interpolations practical algorithms for multivariate higher order splines"

Connected through hyperedge HE-4 (induced by source paper "flight dynamics based recovery of a uav trajectory using ground cameras"):
  with members:
    - "a comparative study of object trackers for infrared flying bird tracking"
    - "flying objects detection from a single moving camera"
    - "multiple object tracking a literature review"

Connected through hyperedge HE-5 (induced by source paper "real time stochastic kinodynamic motion planning via multiobjective search on gpus"):
  with members:
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "optimal sampling based motion planning under differential constraints the driftless case"

Connected through hyperedge HE-6 (induced by source paper "a convex optimization approach to smooth trajectories for motion planning with car like robots"):
  with members:
    - "optimal sampling based motion planning under differential constraints the driftless case"

Connected through hyperedge HE-7 (induced by source paper "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"):
  with members:
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "fast marching tree a fast marching sampling based method for optimal motion planning in many dimensions"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "optimal sampling based motion planning under differential constraints the driftless case"):
  with members:
    - "asymptotically optimal sampling based kinodynamic planning"
    - "fast marching tree a fast marching sampling based method for optimal motion planning in many dimensions"
    - "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)