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: "sampling based bottleneck pathfinding with applications to frechet matching"
  Abstract: We describe a general probabilistic framework to address a variety of Frechet-distance optimization problems. Specifically, we are interested in finding minimal bottleneck-paths in $d$-dimensional Euclidean space between given start and goal points, namely paths that minimize the maximal value over a continuous cost map. We present an efficient and simple sampling-based framework for this problem, which is inspired by, and draws ideas from, techniques for robot motion planning. We extend the framework to handle not only standard bottleneck pathfinding, but also the more demanding case, where the path needs to be monotone in all dimensions. Finally, we provide experimental results of the framework on several types of problems.

Connected through hyperedge HE-1 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space"):
  with members:
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "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"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "minimizing task space frechet error via efficient incremental graph search"):
  with members:
    - "the provable virtue of laziness in motion planning"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "efficient sampling based bottleneck pathfinding over cost maps"):
  with members:
    - "an efficient algorithm for computing high quality paths amid polygonal obstacles"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "time optimal coordination of mobile robots along specified paths"
    - "on the complexity of an unregulated traffic crossing"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"

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.CG(Computational Geometry)