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: "fast object learning and dual arm coordination for cluttered stowing picking and packing"
  Abstract: Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.

Connected through hyperedge HE-1 (induced by source paper "joint learning of instance and semantic segmentation for robotic pick and place with heavy occlusions in clutter"):
  with members:
    - "probabilistic 3d multilabel real time mapping for multi object manipulation"
    - "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "joint learning of intrinsic images and semantic segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "kpam keypoint affordances for category level robotic manipulation"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "category level 6d object pose recovery in depth images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning dexterous in hand manipulation"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization"

Connected through hyperedge HE-3 (induced by source paper "towards robust product packing with a minimalistic end effector"):
  with members:
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search"
    - "mask r cnn"
    - "analysis and observations from the first amazon picking challenge"
    - "global hypothesis generation for 6d object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance"):
  with members:
    - "nimbro rescue solving disaster response tasks through mobile manipulation robot momaro"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "autonomous dual arm manipulation of familiar objects"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "anytime hybrid driving stepping locomotion planning"
    - "point set registration coherent point drift"

Connected through hyperedge HE-5 (induced by source paper "dense object nets learning dense visual object descriptors by and for robotic manipulation"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"
    - "semantic segmentation from limited training data"

Connected through hyperedge HE-6 (induced by source paper "robust 6d object pose estimation in cluttered scenes using semantic segmentation and pose regression networks"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "aggregated residual transformations for deep neural networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"):
  with members:
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "anytime hybrid driving stepping locomotion planning"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "autonomous dual arm manipulation of familiar objects"):
  with members:
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)