human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lego leveraging experience in roadmap generation for sampling based planning"):
  Abstract: We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low-cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. #R##N#We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficult-to-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.

Includes members:
  Member "using local experiences for global motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learned critical probabilistic roadmaps for robotic motion planning")
  Member "robot motion planning in learned latent spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "deep sequential models for sampling based planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "deeply informed neural sampling for robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path")
  Member "learning implicit sampling distributions for motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners")
  Member "approximate inference based motion planning by learning and exploiting low dimensional latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive path integral autoencoder representation learning and planning for dynamical systems")
  Member "domain randomization for transferring deep neural networks from simulation to the real world":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "generating long term trajectories using deep hierarchical networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")

Question: Please tell me which class the source paper 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)