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 "the surprising effectiveness of linear models for visual foresight in object pile manipulation"):
  Abstract: In this paper, we tackle the problem of pushing piles of small objects into a desired target set using visual feedback. Unlike conventional single-object manipulation pipelines, which estimate the state of the system parametrized by pose, the underlying physical state of this system is difficult to observe from images. Thus, we take the approach of reasoning directly in the space of images, and acquire the dynamics of visual measurements in order to synthesize a visual-feedback policy. We present a simple controller using an image-space Lyapunov function, and evaluate the closed-loop performance using three different class of models for image prediction: deep-learning-based models for image-to-image translation, an object-centric model obtained from treating each pixel as a particle, and a switched-linear system where an action-dependent linear map is used. Through results in simulation and experiment, we show that for this task, a linear model works surprisingly well -- achieving better prediction error, downstream task performance, and generalization to new environments than the deep models we trained on the same amount of data. We believe these results provide an interesting example in the spectrum of models that are most useful for vision-based feedback in manipulation, considering both the quality of visual prediction, as well as compatibility with rigorous methods for control design and analysis.

Includes members:
  Member "keto learning keypoint representations for tool manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a geometric perspective on visual imitation learning")
  Member "planning robot motion using deep visual prediction":
    also connected through other hyperedges:
      (none)
  Member "unsupervised learning of object structure and dynamics from videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction")
  Member "object centric forward modeling for model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives")
  Member "learning to manipulate object collections using grounded state representations":
    also connected through other hyperedges:
      (none)
  Member "difftaichi differentiable programming for physical simulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to simulate complex physics with graph networks")
  Member "friction variability in planar pushing data anisotropic friction and data collection bias":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gp sum gaussian processes filtering of non gaussian beliefs")
  Member "reasoning about physical interactions with object oriented prediction and planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")

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)