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 networks for spherical signals"
  Abstract: The success of convolutional networks in learning problems involving planar signals such as images is due to their ability to exploit the translation symmetry of the data distribution through weight sharing. Many areas of science and egineering deal with signals with other symmetries, such as rotation invariant data on the sphere. Examples include climate and weather science, astrophysics, and chemistry. In this paper we present spherical convolutional networks. These networks use convolutions on the sphere and rotation group, which results in rotational weight sharing and rotation equivariance. Using a synthetic spherical MNIST dataset, we show that spherical convolutional networks are very effective at dealing with rotationally invariant classification problems.

Connected through hyperedge HE-1 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the limitations of representing functions on sets"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "simplicial closure and higher order link prediction"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks on non uniform geometrical signals using euclidean spectral transformation"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "dynamic graph cnn for learning on point clouds"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-3 (induced by source paper "poirot a rotation invariant omni directional pointnet"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "a cnn for homogneous riemannian manifolds with applications to neuroimaging"
    - "splatnet sparse lattice networks for point cloud processing"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"

Connected through hyperedge HE-4 (induced by source paper "pano popups indoor 3d reconstruction with a plane aware network"):
  with members:
    - "convolutions on spherical images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "megadepth learning single view depth prediction from internet photos"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"

Connected through hyperedge HE-5 (induced by source paper "scale equivariant steerable networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "scale steerable filters for locally scale invariant convolutional neural networks"
    - "harmonic networks with limited training samples"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant transformer networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "mapped convolutions"):
  with members:
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "deformable convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "low memory gemm based convolution algorithms for deep neural networks"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-7 (induced by source paper "surreal complex valued deep learning as principled transformations on a rotational lie group"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "manifoldnet a deep network framework for manifold valued data"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "polar transformer networks"
    - "deep complex networks"
    - "geometric deep learning going beyond euclidean data"
    - "joint embedding and classification for sar target recognition"
    - "harmonic networks deep translation and rotation equivariance"

Connected through hyperedge HE-8 (induced by source paper "tangent images for mitigating spherical distortion"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "mapped convolutions"
    - "convolutions on spherical images"
    - "kernel transformer networks for compact spherical convolution"
    - "learning so 3 equivariant representations with spherical cnns"
    - "joint 2d 3d semantic data for indoor scene understanding"

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.LG(Machine Learning)