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: "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
  Abstract: Rapidly-exploring random trees (RRTs) are pop- ular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only inefficient but also inconsistent with their single-query nature. For problems seeking to minimize path length, the subset of states that can improve a solution can be described by a prolate hyperspheroid. We show that unless this subset is sam- pled directly, the probability of improving a solution becomes arbitrarily small in large worlds or high state dimensions. In this paper, we present an exact method to focus the search by directly sampling this subset. The advantages of the presented sampling technique are demonstrated with a new algorithm, Informed RRT*. This method retains the same probabilistic guarantees on complete- ness and optimality as RRT* while improving the convergence rate and final solution quality. We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms. We show exper- imentally that it outperforms RRT* in rate of convergence, final solution cost, and ability to find difficult passages while demonstrating less dependence on the state dimension and range of the planning problem.

Connected through hyperedge HE-1 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "leveraging experience in lazy search"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "motion planning networks"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "an asymptotically optimal sampling based algorithm for bi directional motion planning"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "gpu parallelization of policy iteration rrt"):
  with members:
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "gunrock gpu graph analytics"
    - "incremental sampling based motion planners using policy iteration methods"
    - "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-5 (induced by source paper "fast frontier based information driven autonomous exploration with an mav"):
  with members:
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"

Connected through hyperedge HE-6 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"

Connected through hyperedge HE-7 (induced by source paper "learning to plan via neural exploration exploitation trees"):
  with members:
    - "learning to search with mctsnets"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep sequential models for sampling based planning"
    - "gated path planning networks"
    - "learning implicit sampling distributions for motion planning"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning sampling distributions for robot motion planning"

Connected through hyperedge HE-8 (induced by source paper "a hierarchical network for diverse trajectory proposals"):
  with members:
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "edge slam edge points based monocular visual slam"
    - "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"
    - "end to end training of deep visuomotor policies"
    - "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)