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: "multi sensor surface analysis for robotic ironing"
  Abstract: Robotic manipulation of deformable objects remains a challenging task. One such task is to iron a piece of cloth autonomously. Given a roughly flattened cloth, the goal is to have an ironing plan that can iteratively apply a regular iron to remove all the major wrinkles by a robot. We present a novel solution to analyze the cloth surface by fusing two surface scan techniques: a curvature scan and a discontinuity scan. The curvature scan can estimate the height deviation of the cloth surface, while the discontinuity scan can effectively detect sharp surface features, such as wrinkles. We use this information to detect the regions that need to be pulled and extended before ironing, and the other regions where we want to detect wrinkles and apply ironing to remove the wrinkles. We demonstrate that our hybrid scan technique is able to capture and classify wrinkles over the surface robustly. Given detected wrinkles, we enable a robot to iron them using shape features. Experimental results show that using our wrinkle analysis algorithm, our robot is able to iron the cloth surface and effectively remove the wrinkles.

Connected through hyperedge HE-1 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "model based behavioral cloning with future image similarity learning"
    - "learning to manipulate deformable objects without demonstrations"
    - "dynamic cloth manipulation with deep reinforcement learning"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "deep haptic model predictive control for robot assisted dressing"

Connected through hyperedge HE-2 (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"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "learning dexterous manipulation for a soft robotic hand from human demonstration"
    - "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-3 (induced by source paper "cloth manipulation using random forest based controller parametrization"):
  with members:
    - "folding deformable objects using predictive simulation and trajectory optimization"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-4 (induced by source paper "visual closed loop control for pouring liquids"):
  with members:
    - "lstm a search space odyssey"
    - "guided policy search with delayed senor measurements"
    - "towards learning to perceive and reason about liquids"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"
    - "learning to place new objects in a scene"

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)