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: "experience based planning with sparse roadmap spanners"
  Abstract: We present an experienced-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces that include many invariant constraints, such as those found with whole body humanoid motion planning. Experiences are generated using probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which provides asymptotically near-optimal coverage of the configuration space, making storing, retrieving, and repairing past experiences very efficient with respect to memory and time. The Thunder framework improves upon past experience-based planners by storing experiences in a graph rather than in individual paths, eliminating redundant information, providing more opportunities for path reuse, and providing a theoretical limit to the size of the experience graph. These properties also lead to improved handling of dynamically changing environments, reasoning about optimal paths, and reducing query resolution time. The approach is demonstrated on a 30 degrees of freedom humanoid robot and compared with the Lightning framework, an experience-based planner that uses individual paths to store past experiences. In environments with variable obstacles and stability constraints, experiments show that Thunder is on average an order of magnitude faster than Lightning and planning from scratch. Thunder also uses 98.8% less memory to store its experiences after 10,000 trials when compared to Lightning. Our framework is implemented and freely available in the Open Motion Planning Library.

Connected through hyperedge HE-1 (induced by source paper "provably constant time planning and re planning for real time grasping objects off a conveyor"):
  with members:
    - "planning learning and reasoning framework for robot truck unloading"
    - "toward fast and optimal robotic pick and place on a moving conveyor"

Connected through hyperedge HE-2 (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-3 (induced by source paper "using local experiences for global motion planning"):
  with members:
    - "learning to guide task and motion planning using score space representation"
    - "learning sampling distributions for robot motion planning"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"

Connected through hyperedge HE-4 (induced by source paper "provable infinite horizon real time planning for repetitive tasks"):
  with members:
    - "landmark guided probabilistic roadmap queries"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-5 (induced by source paper "deeply informed neural sampling for robot motion planning"):
  with members:
    - "motion planning networks"
    - "potential functions based sampling heuristic for optimal path planning"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "diameter spanners eccentricity spanners and approximating extremal distances"):
  with members:
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "linear size distance preservers"
    - "the 4 3 additive spanner exponent is tight"
    - "the emergence of sparse spanners and greedy well separated pair decomposition"

Connected through hyperedge HE-7 (induced by source paper "improving trajectory optimization using a roadmap framework"):
  with members:
    - "funnel libraries for real time robust feedback motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "learning sampling distributions for robot motion planning"):
  with members:
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "fixing a broken elbo"
    - "generalizing informed sampling for asymptotically optimal sampling based kinodynamic planning via markov chain monte carlo"
    - "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"
    - "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)