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 algorithms for optimal motion planning"
  Abstract: During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"

Connected through hyperedge HE-3 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision"):
  with members:
    - "self driving cars a survey"
    - "learning human aware path planning with fully convolutional networks"
    - "graph r cnn for scene graph generation"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "a parametric top view representation of complex road scenes"
    - "pixels to graphs by associative embedding"

Connected through hyperedge HE-4 (induced by source paper "target driven visual navigation exploiting object relationships"):
  with members:
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "deduce diverse scene detection methods in unseen challenging environments"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "looking at the right stuff guided semantic gaze for autonomous driving"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "on evaluation of embodied navigation agents"
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "learning novel policies for tasks"
    - "an algorithmic perspective on imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "divide and conquer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deep compositional robotic planners that follow natural language commands"):
  with members:
    - "from semantics to execution integrating action planning with reinforcement learning for robotic causal problem solving"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "from semantics to execution integrating action planning with reinforcement learning for robotic tool use"
    - "prospection interpretable plans from language by predicting the future"
    - "learning to plan via neural exploration exploitation trees"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "deep sequential models for sampling based planning"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "gomp grasp optimized motion planning for bin picking"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "manipulation trajectory optimization with online grasp synthesis and selection"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "grasp pose detection in point clouds"

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)