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: "roto translation covariant convolutional networks for medical image analysis"
  Abstract: We propose a framework for rotation and translation covariant deep learning using $SE(2)$ group convolutions. The group product of the special Euclidean motion group $SE(2)$ describes how a concatenation of two roto-translations results in a net roto-translation. We encode this geometric structure into convolutional neural networks (CNNs) via $SE(2)$ group convolutional layers, which fit into the standard 2D CNN framework, and which allow to generically deal with rotated input samples without the need for data augmentation. #R##N#We introduce three layers: a lifting layer which lifts a 2D (vector valued) image to an $SE(2)$-image, i.e., 3D (vector valued) data whose domain is $SE(2)$; a group convolution layer from and to an $SE(2)$-image; and a projection layer from an $SE(2)$-image to a 2D image. The lifting and group convolution layers are $SE(2)$ covariant (the output roto-translates with the input). The final projection layer, a maximum intensity projection over rotations, makes the full CNN rotation invariant. #R##N#We show with three different problems in histopathology, retinal imaging, and electron microscopy that with the proposed group CNNs, state-of-the-art performance can be achieved, without the need for data augmentation by rotation and with increased performance compared to standard CNNs that do rely on augmentation.

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 "roto translation equivariant convolutional networks application to histopathology image analysis"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "b spline cnns on lie groups"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "3d g cnns for pulmonary nodule detection"
    - "computing cnn loss and gradients for pose estimation with riemannian geometry"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "a general theory of equivariant cnns on homogeneous spaces"

Connected through hyperedge HE-3 (induced by source paper "gauge equivariant mesh cnns anisotropic convolutions on geometric graphs"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "spherical u net on cortical surfaces methods and applications"
    - "spiralnet a fast and highly efficient mesh convolution operator"
    - "zernet convolutional neural networks on arbitrary surfaces via zernike local tangent space estimation"
    - "parallel transport convolution a new tool for convolutional neural networks on manifolds"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-4 (induced by source paper "pde based group equivariant convolutional neural networks"):
  with members:
    - "kercnns biologically inspired lateral connections for classification of corrupted images"
    - "a cortical inspired model for orientation dependent contrast perception a link with wilson cowan equations"
    - "3d g cnns for pulmonary nodule detection"
    - "stable architectures for deep neural networks"
    - "cubenet equivariance to 3d rotation and translation"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "learning steerable filters for rotation equivariant cnns"
    - "learning so 3 equivariant representations with spherical cnns"

Connected through hyperedge HE-5 (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"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "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-6 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "general e 2 equivariant steerable cnns"):
  with members:
    - "spherical cnns on unstructured grids"
    - "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"
    - "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-8 (induced by source paper "b spline cnns on lie groups"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "scale aware trident networks for object detection"
    - "group equivariant capsule networks"
    - "3d g cnns for pulmonary nodule detection"
    - "template matching via densities on the roto translation group"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

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)