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: "efficient high quality motion planning by fast all pairs r nearest neighbors"
  Abstract: Sampling-based motion-planning algorithms typically rely on nearest-neighbor (NN) queries when constructing a roadmap. Recent results suggest that in various settings NN queries may be the computational bottleneck of such algorithms. Moreover, in several asymptotically-optimal algorithms these NN queries are of a specific form: Given a set of points and a radius r report all pairs of points whose distance is at most r. This calls for an application-specific NN data structure tailored to efficiently answering this type of queries. Randomly transformed grids (RTG) were recently proposed by Aiger et al. as a tool to answer such queries and have been shown to outperform common implementations of NN data structures in this context. In this work we employ RTG for sampling-based motion-planning algorithms and describe an efficient implementation of the approach. We show that for motion-planning, RTG allow for faster convergence to high-quality solutions when compared with existing NN data structures. Additionally, RTG enable significantly shorter construction times for batched-PRM variants; specifically, we demonstrate a speedup by a factor of two to three for some scenarios.

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"
    - "asymptotically near optimal rrt for fast high quality motion planning"

Connected through hyperedge HE-2 (induced by source paper "similarity kernel and clustering via random projection forests"):
  with members:
    - "incorporating deep features in the analysis of tissue microarray images"
    - "clustering millions of faces by identity"
    - "similarity learning for high dimensional sparse data"
    - "a survey on metric learning for feature vectors and structured data"
    - "a survey on multi view learning"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "sampling based bottleneck pathfinding with applications to frechet matching"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "computing the frechet distance with a retractable leash"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "on the hardness of unlabeled multi robot motion planning"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "simple curve embedding"

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"
    - "sampling based bottleneck pathfinding with applications to frechet matching"
    - "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 multi robot motion planning for unlabeled discs in simple polygons"

Connected through hyperedge HE-5 (induced by source paper "new perspective on sampling based motion planning via random geometric graphs"):
  with members:
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "on the relation between graph distance and euclidean distance in random geometric graphs"
    - "on the hardness of unlabeled multi robot motion planning"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "optimal data dependent hashing for approximate near neighbors"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "optimal randomized incremental construction for guaranteed logarithmic planar point location"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "asymptotically optimal motion planning using lower bounds on cost"):
  with members:
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "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 "asymptotically near optimal rrt for fast high quality motion planning"):
  with members:
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "sparsification of motion planning roadmaps by edge contraction"
    - "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)