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: "manipulating highly deformable materials using a visual feedback dictionary"
  Abstract: The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.

Connected through hyperedge HE-1 (induced by source paper "learning when to trust a dynamics model for planning in reduced state spaces"):
  with members:
    - "manipulating deformable objects by interleaving prediction planning and control"
    - "learning robotic manipulation through visual planning and acting"
    - "robot motion planning in learned latent spaces"
    - "learning latent space dynamics for tactile servoing"
    - "robust locally linear controllable embedding"
    - "deep visual foresight for planning robot motion"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a grasping centered analysis for cloth manipulation"):
  with members:
    - "folding deformable objects using predictive simulation and trajectory optimization"

Connected through hyperedge HE-3 (induced by source paper "cloth manipulation using random forest based imitation learning"):
  with members:
    - "cloth manipulation using random forest based controller parametrization"
    - "learning visual servoing with deep features and fitted q iteration"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning dexterous manipulation for a soft robotic hand from human demonstration"
    - "multi sensor surface analysis for robotic ironing"
    - "folding deformable objects using predictive simulation and trajectory optimization"
    - "end to end training of deep visuomotor policies"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "fast motion planning for high dof robot systems using hierarchical system identification"):
  with members:
    - "deep haptic model predictive control for robot assisted dressing"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "learning to navigate cloth using haptics"
    - "proximal policy optimization algorithms"
    - "kinodynamic rrt optimal motion planning for systems with linear differential constraints"
    - "two hierarchies of spline interpolations practical algorithms for multivariate higher order splines"

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)