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: "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
  Abstract: Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group geometry simultaneously, namely a rotation-equivariant CNN with decomposed convolutional filters (RotDCF). This decomposition facilitates computing the joint convolution, which is proved to be necessary for the group equivariance. It significantly reduces the model size and computational complexity while preserving performance, and truncation of the bases expansion serves implicitly to regularize the filters. On datasets involving in-plane and out-of-plane object rotations, RotDCF deep features demonstrate greater robustness and interpretability than regular CNNs. The stability of the equivariant representation to input variations is also proved theoretically under generic assumptions on the filters in the decomposed form. The RotDCF framework can be extended to groups other than rotations, providing a general approach which achieves both group equivariance and representation stability at a reduced model size.

Connected through hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "building deep equivariant capsule networks"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "stand alone self attention in vision models"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "attention augmented convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "directional message passing for molecular graphs"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the convergence of adam and beyond"
    - "provably powerful graph networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-3 (induced by source paper "local rotation invariance in 3d cnns"):
  with members:
    - "b spline cnns on lie groups"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "rotational 3d texture classification using group equivariant cnns"
    - "cubenet equivariance to 3d rotation and translation"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "learning steerable filters for rotation equivariant cnns"
    - "harmonic networks deep translation and rotation equivariance"

Connected through hyperedge HE-4 (induced by source paper "deep learning for 2d and 3d rotatable data an overview of methods"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "learning local rgb to cad correspondences for object pose estimation"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "3d human pose estimation with siamese equivariant embedding"
    - "cubenet equivariance to 3d rotation and translation"
    - "real time deep pose estimation with geodesic loss for image to template rigid registration"

Connected through hyperedge HE-5 (induced by source paper "scale equivariant neural networks with decomposed convolutional filters"):
  with members:
    - "selective kernel networks"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "scale equivariance in cnns with vector fields"
    - "learning steerable filters for rotation equivariant cnns"
    - "oriented response networks"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "translation insensitive cnns"):
  with members:
    - "spatially adaptive filter units for compact and efficient deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "making convolutional networks shift invariant again"
    - "a systematic framework for natural perturbations from videos"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "emergence of invariance and disentanglement in deep representations"
    - "structured receptive fields in cnns"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)