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: "spherical cnns on unstructured grids"
  Abstract: We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. To this end, we replace conventional convolution kernels with linear combinations of differential operators that are weighted by learnable parameters. Differential operators can be efficiently estimated on unstructured grids using one-ring neighbors, and learnable parameters can be optimized through standard back-propagation. As a result, we obtain extremely efficient neural networks that match or outperform state-of-the-art network architectures in terms of performance but with a significantly lower number of network parameters. We evaluate our algorithm in an extensive series of experiments on a variety of computer vision and climate science tasks, including shape classification, climate pattern segmentation, and omnidirectional image semantic segmentation. Overall, we present (1) a novel CNN approach on unstructured grids using parameterized differential operators for spherical signals, and (2) we show that our unique kernel parameterization allows our model to achieve the same or higher accuracy with significantly fewer network parameters.

Connected through hyperedge HE-1 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "underwater fish detection with weak multi domain supervision"
    - "datasets for face and object detection in fisheye images"
    - "spherical u net on cortical surfaces methods and applications"
    - "tagslam robust slam with fiducial markers"
    - "convolutions on spherical images"
    - "reprojection r cnn a fast and accurate object detector for 360 images"
    - "depth estimation on underwater omni directional images using a deep neural network"

Connected through hyperedge HE-2 (induced by source paper "variable viewpoint representations for 3d object recognition"):
  with members:
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "learning so 3 equivariant representations with spherical cnns"
    - "the expressive power of neural networks a view from the width"
    - "sampling sparse signals on the sphere algorithms and applications"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "shapenet an information rich 3d model repository"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-3 (induced by source paper "meshcnn a network with an edge"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning an effective equivariant 3d descriptor without supervision"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "equivariant neural networks and equivarification"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "riemannian adaptive optimization methods"

Connected through hyperedge HE-5 (induced by source paper "ddsl deep differentiable simplex layer for learning geometric signals"):
  with members:
    - "convolutional neural networks on non uniform geometrical signals using euclidean spectral transformation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning category specific mesh reconstruction from image collections"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "unsupervised training for 3d morphable model regression"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-6 (induced by source paper "general e 2 equivariant steerable cnns"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning to convolve a generalized weight tying approach"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-7 (induced by source paper "gauge equivariant convolutional networks and the icosahedral cnn"):
  with members:
    - "parallel transport convolution a new tool for convolutional neural networks on manifolds"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "invariant and equivariant graph networks"
    - "a general theory of equivariant cnns on homogeneous spaces"

Connected through hyperedge HE-8 (induced by source paper "orientation aware semantic segmentation on icosahedron spheres"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "cubenet equivariance to 3d rotation and translation"
    - "kernel transformer networks for compact spherical convolution"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"

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)