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: "asymptotically near optimal rrt for fast high quality motion planning"
  Abstract: We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based algorithm that is asymptotically near-optimal. Namely, the solution extracted from LBT-RRT converges to a solution that is within an approximation factor of 1+epsilon of the optimal solution. Our algorithm allows for a continuous interpolation between the fast RRT algorithm and the asymptotically optimal RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no approximation is allowed), LBT-RRT behaves like RRG. When the approximation factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to produce paths that have higher quality than RRT would produce and run faster than RRT* would run. This is done by maintaining a tree which is a sub-graph of the RRG roadmap and a second, auxiliary graph, which we call the lower-bound graph. The combination of the two roadmaps, which is faster to maintain than the roadmap maintained by RRT*, efficiently guarantees asymptotic near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion planning. We demonstrate the performance of the algorithm for scenarios ranging from 3 to 12 degrees of freedom and show that even for small approximation factors, the algorithm produces high-quality solutions (comparable to RRG and RRT*) with little running-time overhead when compared to RRT.

Connected through hyperedge HE-1 (induced by source paper "batch informed trees bit 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"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"

Connected through hyperedge HE-2 (induced by source paper "asymptotically optimal sampling based planners"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "anytime multi arm task and motion planning for pick and place of individual objects via handoffs"
    - "revisiting the asymptotic optimality of rrt"
    - "robot motion planning in learned latent spaces"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "fast anytime motion planning for prehensile manipulation in clutter"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "the provable virtue of laziness in motion planning"

Connected through hyperedge HE-3 (induced by source paper "toward asymptotically optimal inspection planning via efficient near optimal graph search"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "efficient motion planning for problems lacking optimal substructure"
    - "the provable virtue of laziness in motion planning"
    - "online interactive user guidance for high dimensional constrained motion planning"
    - "effective footstep planning for humanoids using homotopy class guidance"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-4 (induced by source paper "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"):
  with members:
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "informed sampling for asymptotically optimal path planning"
    - "the critical radius in sampling based motion planning"
    - "efficient sampling based bottleneck pathfinding over cost maps"
    - "completeness of randomized kinodynamic planners with state based steering"
    - "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"
    - "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems"

Connected through hyperedge HE-5 (induced by source paper "object detection and motion planning for automated welding of tubular joints"):
  with members:
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "direct off line robot programming via a common cad package"
    - "difference of normals as a multi scale operator in unorganized point clouds"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "informed sampling for asymptotically optimal path planning"):
  with members:
    - "informed asymptotically optimal anytime search"
    - "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"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (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"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"

Connected through hyperedge HE-8 (induced by source paper "informed asymptotically optimal anytime search"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion 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"

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)