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: "partial functional correspondence"
  Abstract: In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes. We use perturbation analysis to show how removal of shape parts changes the Laplace-Beltrami eigenfunctions, and exploit it as a prior on the spectral representation of the correspondence. Corresponding parts are optimization variables in our problem and are used to weight the functional correspondence; we are looking for the largest and most regular (in the Mumford-Shah sense) parts that minimize correspondence distortion. We show that our approach can cope with very challenging correspondence settings.

Connected through hyperedge HE-1 (induced by source paper "cyclic functional mapping self supervised correspondence between non isometric deformable shapes"):
  with members:
    - "lbs autoencoder self supervised fitting of articulated meshes to point clouds"
    - "3d coded 3d correspondences by deep deformation"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "geodesic convolutional neural networks on riemannian manifolds"

Connected through hyperedge HE-2 (induced by source paper "unsupervised deep learning for structured shape matching"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "dense human body correspondences using convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep functional dictionaries learning consistent semantic structures on 3d models from functions"):
  with members:
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "what makes for effective detection proposals"
    - "action recognition in video using sparse coding and relative features"

Connected through hyperedge HE-4 (induced by source paper "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"):
  with members:
    - "functional correspondence by matrix completion"

Connected through hyperedge HE-5 (induced by source paper "signature of geometric centroids for 3d local shape description and partial shape matching"):
  with members:
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-6 (induced by source paper "point wise map recovery and refinement from functional correspondence"):
  with members:
    - "functional correspondence by matrix completion"
    - "coupled quasi harmonic bases"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

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.CV(Computer Vision and Pattern Recognition)