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: "convolutional neural networks on 3d surfaces using parallel frames"
  Abstract: We extend Convolutional Neural Networks (CNNs) on flat and regular domains (e.g. 2D images) to curved surfaces embedded in 3D Euclidean space that are discretized as irregular meshes and widely used to represent geometric data in Computer Vision and Graphics. We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces with the canonical parallel transport that preserves metric. For computation, we rely on a parallel N-direction frame field on the surface that minimizes field variation and therefore is as compatible as possible to and approximates the parallel transport. On the tangent spaces equipped with parallel frames, the computation of surface convolution becomes standard routine. The frames have rotational symmetry which we disambiguate by constructing the covering space of surface induced by the parallel frames and grouping the feature maps into N sets accordingly; convolution is computed on the N branches of the cover space with respective feature maps while the kernel weights are shared. To handle irregular points of a discrete mesh while sharing kernel weights, we make the convolution semi-discrete, i.e. the convolution kernels are polynomial functions, and their convolution with discrete surface points becomes sampling and weighted summation. Pooling and unpooling operations are computed along a mesh hierarchy built through simplification. The presented surface CNNs allow effective deep learning on meshes. We show that for tasks of classification, segmentation and non-rigid registration, surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "deep learning for 3d point clouds a survey"
    - "bsp net generating compact meshes via binary space partitioning"
    - "pixel2mesh multi view 3d mesh generation via deformation"

Connected through hyperedge HE-2 (induced by source paper "fpconv learning local flattening for point convolution"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "deepgcns can gcns go as deep as cnns"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"

Connected through hyperedge HE-3 (induced by source paper "hierarchical point edge interaction network for point cloud semantic segmentation"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-4 (induced by source paper "4d spatio temporal convnets minkowski convolutional neural networks"):
  with members:
    - "modeling 4d fmri data via spatio temporal convolutional neural networks st cnn"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "tangent convolutions for dense prediction in 3d"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-5 (induced by source paper "surface networks via general covers"):
  with members:
    - "multi chart generative surface modeling"
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"

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)