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: "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
  Abstract: In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT).

Connected through hyperedge HE-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning"):
  with members:
    - "scalable asymptotically optimal multi robot motion planning"
    - "effective metrics for multi robot motion planning"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "k color multi robot motion planning"

Connected through hyperedge HE-2 (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"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "asymptotically near optimal rrt for fast high quality motion planning"

Connected through hyperedge HE-4 (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"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
    - "an asymptotically optimal sampling based algorithm for bi directional motion planning"
    - "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-5 (induced by source paper "learn and link learning critical regions for efficient planning"):
  with members:
    - "learning sampling distributions for robot motion planning"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"
    - "ordered landmarks in planning"

Connected through hyperedge HE-6 (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"
    - "sampling based bottleneck pathfinding with applications to frechet matching"
    - "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-7 (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"
    - "an asymptotically optimal sampling based algorithm for bi directional motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "advanced bit abit sampling based planning with advanced graph search techniques"):
  with members:
    - "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics"
    - "batch informed trees bit informed asymptotically optimal anytime search"
    - "informed sampling for asymptotically optimal path planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "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)