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: "transferring grasping skills to novel instances by latent space non rigid registration"
  Abstract: Robots acting in open environments need to be able to handle novel objects. Based on the observation that objects within a category are often similar in their shapes and usage, we propose an approach for transferring grasping skills from known instances to novel instances of an object category. Correspondences between the instances are established by means of a non-rigid registration method that combines the Coherent Point Drift approach with subspace methods. The known object instances are modeled using a canonical shape and a transformation which deforms it to match the instance shape. The principle axes of variation of these deformations define a low-dimensional latent space. New instances can be generated through interpolation and extrapolation in this shape space. For inferring the shape parameters of an unknown instance, an energy function expressed in terms of the latent variables is minimized. Due to the class-level knowledge of the object, our method is able to complete novel shapes from partial views. Control poses for generating grasping motions are transferred efficiently to novel instances by the estimated non-rigid transformation.

Connected through hyperedge HE-1 (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"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"

Connected through hyperedge HE-2 (induced by source paper "autonomous bimanual functional regrasping of novel object class instances"):
  with members:
    - "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "light weight refinenet for real time semantic segmentation"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning postural synergies for categorical grasping through shape space registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-3 (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"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "autonomous dual arm manipulation of familiar objects"
    - "anytime hybrid driving stepping locomotion planning"
    - "point set registration coherent point drift"

Connected through hyperedge HE-4 (induced by source paper "robust grasp planning over uncertain shape completions"):
  with members:
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "multi modal geometric learning for grasping and manipulation"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "shape completion enabled robotic grasping"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "flexible disaster response of tomorrow final presentation and evaluation of the centauro system"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "transferring category based functional grasping skills by latent space non rigid registration"
    - "autonomous dual arm manipulation of familiar objects"
    - "anytime hybrid driving stepping locomotion planning"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (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"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "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-7 (induced by source paper "learning postural synergies for categorical grasping through shape space registration"):
  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"
    - "autonomous dual arm manipulation of familiar objects"
    - "compact part based shape spaces for dense correspondences"
    - "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"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "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)