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: "an asymptotically optimal sampling based algorithm for bi directional motion planning"
  Abstract: Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bi-directional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners.

Connected through hyperedge HE-1 (induced by source paper "experimental comparison of global motion planning algorithms for wheeled mobile robots"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "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"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "fast planning over roadmaps via selective densification"):
  with members:
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "the provable virtue of laziness in motion planning"
    - "anytime motion planning on large dense roadmaps with expensive edge evaluations"
    - "densification strategies for anytime motion planning over large dense roadmaps"
    - "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-3 (induced by source paper "sample complexity of probabilistic roadmaps via e nets"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "toward asymptotically optimal inspection planning via efficient near optimal graph search"
    - "robot motion planning in learned latent spaces"
    - "fast anytime motion planning for prehensile manipulation in clutter"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "learning sampling distributions for robot motion planning"
    - "the critical radius in sampling based motion planning"
    - "data driven planning via imitation learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "forward kinematics kernel for improved proxy collision checking"):
  with members:
    - "learning based proxy collision detection for robot motion planning applications"
    - "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-6 (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-7 (induced by source paper "group marching tree sampling based approximately optimal motion planning on gpus"):
  with members:
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "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"
    - "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)