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: "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
  Abstract: The complexity of nearest-neighbor search dominates the asymptotic running time of many sampling-based motion-planning algorithms. However, collision detection is often considered to be the computational bottleneck in practice. Examining various asymptotically optimal planning algorithms, we characterize settings, which we call NN-sensitive, in which the practical computational role of nearest-neighbor search is far from being negligible, i.e., the portion of running time taken up by nearest-neighbor search is comparable, or sometimes even greater than the portion of time taken up by collision detection. This reinforces and substantiates the claim that motion-planning algorithms could significantly benefit from efficient and possibly specifically-tailored nearest-neighbor data structures. The asymptotic (near) optimality of these algorithms relies on a prescribed connection radius, defining a ball around a configuration $q$, such that $q$ needs to be connected to all other configurations in that ball. To facilitate our study, we show how to adapt this radius to non-Euclidean spaces, which are prevalent in motion planning. This technical result is of independent interest, as it enables to compare the radial-connection approach with the common alternative, namely, connecting each configuration to its $k$ nearest neighbors ($k$-NN). Indeed, as we demonstrate, there are scenarios where using the radial connection scheme, a solution path of a specific cost is produced ten-fold (and more) faster than with $k$-NN.

Connected through hyperedge HE-1 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics"):
  with members:
    - "batch informed trees bit informed asymptotically optimal anytime search"
    - "sampling based optimal kinodynamic planning with motion primitives"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "informed sampling for asymptotically optimal path planning"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "proximity queries for absolutely continuous parametric curves"):
  with members:
    - "intent aware probabilistic trajectory estimation for collision prediction with uncertainty quantification"
    - "julia a fresh approach to numerical computing"
    - "julia a fast dynamic language for technical computing"

Connected through hyperedge HE-4 (induced by source paper "the critical radius in sampling based motion planning"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "scalable asymptotically optimal multi robot motion planning"
    - "minimizing task space frechet error via efficient incremental graph search"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-5 (induced by source paper "informed asymptotically optimal anytime search"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "incremental sampling based motion planners using policy iteration methods"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "asymptotically near optimal rrt for fast high quality motion planning"

Connected through hyperedge HE-6 (induced by source paper "anytime motion planning on large dense roadmaps with expensive edge evaluations"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "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)