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: "h cnns convolutional neural networks for riemannian homogeneous spaces"
  Abstract: Convolutional neural networks are ubiquitous in Machine Learning applications for solving a variety of problems. They however can not be used as is when data naturally reside on commonly encountered manifolds such as the sphere, the special orthogonal group, the Grassmanian, the manifold of symmetric positive definite matrices and others. Most recently, generalization of CNNs to data residing on a sphere has been reported by some research groups, which go by several names but we will refer to them as spherical CNNs (SCNNs). The key property of SCNNs distinct from the standard CNNs is that they exhibit the rotational equivariance property. In this paper, we theoretically generalize the SCNNs to Riemannian homogeneous manifolds, that include many commonly encountered manifolds including the aforementioned example manifolds. Proof of concept experiments involving synthetic data generated on the manifold of $(3 \times 3)$ symmetric positive definite matrices and the product manifold of $\mathbf{R}^+ \times \mathbf{S}^2$ respectively, are presented. These manifolds are commonly encountered in diffusion magnetic resonance imaging, a non-invasive medical imaging modality.

Connected through hyperedge HE-1 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical cnns on unstructured grids"
    - "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"

Connected through hyperedge HE-2 (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"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-3 (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"
    - "polar transformer networks"
    - "deep complex networks"
    - "geometric deep learning going beyond euclidean data"
    - "convolutional networks for spherical signals"
    - "joint embedding and classification for sar target recognition"
    - "harmonic networks deep translation and rotation equivariance"

Connected through hyperedge HE-4 (induced by source paper "dilated convolutional neural networks for sequential manifold valued data"):
  with members:
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "manifoldnet a deep network framework for manifold valued data"
    - "covariant compositional networks for learning graphs"
    - "the statistical recurrent unit"
    - "tensor train recurrent neural networks for video classification"
    - "geometric deep learning going beyond euclidean data"
    - "second order convolutional neural networks"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-5 (induced by source paper "surreal fr echet mean and distance transform for complex valued deep learning"):
  with members:
    - "manifoldnet a deep network framework for manifold valued data"
    - "polar transformer networks"
    - "deep complex networks"
    - "joint embedding and classification for sar target recognition"
    - "harmonic networks deep translation and rotation equivariance"
    - "convolutional radio modulation recognition networks"
    - "group equivariant convolutional networks"
    - "affinity cnn learning pixel centric pairwise relations for figure ground embedding"

Connected through hyperedge HE-6 (induced by source paper "parallel transport convolution a new tool for convolutional neural networks on manifolds"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "deep convolutional networks on graph structured data"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "shapenet convolutional neural networks on non euclidean manifolds"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "manifoldnet a deep network framework for manifold valued data"):
  with members:
    - "polar transformer networks"
    - "building deep networks on grassmann manifolds"
    - "group equivariant convolutional networks"
    - "deep convolutional networks on graph structured data"
    - "representation learning a review and new perspectives"

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)