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 methods for factored task and motion planning"
  Abstract: This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing.

Connected through hyperedge HE-1 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "conceptualization of object compositions using persistent homology"
    - "admissible abstractions for near optimal task and motion planning"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "6 dof object pose from semantic keypoints"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-2 (induced by source paper "scalable and probabilistically complete planning for robotic spatial extrusion"):
  with members:
    - "freeform assembly planning"
    - "automated sequence and motion planning for robotic spatial extrusion of 3d trusses"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "backward forward search for manipulation planning"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (induced by source paper "object centric task and motion planning in dynamic environments"):
  with members:
    - "continuous relaxation of symbolic planner for one shot imitation learning"
    - "combined task and motion planning as classical ai planning"
    - "depth based object tracking using a robust gaussian filter"
    - "probabilistic object tracking using a range camera"
    - "towards combining htn planning and geometric task planning"
    - "the fast downward planning system"

Connected through hyperedge HE-4 (induced by source paper "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"):
  with members:
    - "inferring occluded geometry improves performance when retrieving an object from dense clutter"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "object placement planning and optimization for robot manipulators"
    - "robust grasp planning over uncertain shape completions"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "category level 6d object pose recovery in depth images"

Connected through hyperedge HE-5 (induced by source paper "object rearrangement with nested nonprehensile manipulation actions"):
  with members:
    - "towards robust product packing with a minimalistic end effector"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "automated motion planning for robotic assembly of discrete architectural structures"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "rearrangement with nonprehensile manipulation using deep reinforcement learning"
    - "efficient high quality stack rearrangement"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-6 (induced by source paper "online replanning in belief space for partially observable task and motion problems"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "representing robot task plans as robust logical dynamical systems"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "an anytime algorithm for task and motion mdps"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-7 (induced by source paper "anytime integrated task and motion policies for stochastic environments"):
  with members:
    - "active model learning and diverse action sampling for task and motion planning"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "an anytime algorithm for task and motion mdps"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "deep reinforcement learning in parameterized action space"

Connected through hyperedge HE-8 (induced by source paper "learning quickly to plan quickly using modular meta learning"):
  with members:
    - "modular meta learning"
    - "active model learning and diverse action sampling for task and motion planning"
    - "learning to guide task and motion planning using score space representation"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "modular multitask reinforcement learning with policy sketches"
    - "neural module networks"
    - "adam a method for stochastic optimization"
    - "learning symbolic models of stochastic domains"

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)