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 category based functional grasping skills by latent space non rigid registration"
  Abstract: Objects within a category are often similar in their shape and usage. When we---as humans---want to grasp something, we transfer our knowledge from past experiences and adapt it to novel objects. In this paper, we propose a new approach for transferring grasping skills that accumulates grasping knowledge into a category-level canonical model. Grasping motions for novel instances of the category are inferred from geometric deformations between the observed instance and the canonical shape. Correspondences between the shapes are established by means of a non-rigid registration method that combines the Coherent Point Drift approach with subspace methods. By incorporating category-level information into the registration, we avoid unlikely shapes and focus on deformations actually observed within the category. Control poses for generating grasping motions are accumulated in the canonical model from grasping definitions of known objects. According to the estimated shape parameters of a novel instance, the control poses are transformed towards it. The category-level model makes our method particularly relevant for on-line grasping, where fully-observed objects are not easily available. This is demonstrated through experiments in which objects with occluded handles are successfully grasped.

Connected through hyperedge HE-1 (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"
    - "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"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (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"
    - "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"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (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"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "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-5 (induced by source paper "learning postural synergies for categorical grasping through shape space registration"):
  with members:
    - "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"
    - "compact part based shape spaces for dense correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "autonomous dual arm manipulation of familiar objects"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "transferring grasp configurations using active learning and local replanning"):
  with members:
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "shapenet an information rich 3d model repository"
    - "data driven grasp synthesis a survey"

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)