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: "deep roto translation scattering for object classification"
  Abstract: Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT. We introduce a deep scattering convolution network, with predefined wavelet filters over spatial and angular variables. This representation brings an important improvement to results previously obtained with predefined features over object image databases such as Caltech and CIFAR. The resulting accuracy is comparable to results obtained with unsupervised deep learning and dictionary based representations. This shows that refining image representations by using geometric priors is a promising direction to improve image classification and its understanding.

Connected through hyperedge HE-1 (induced by source paper "pde based group equivariant convolutional neural networks"):
  with members:
    - "kercnns biologically inspired lateral connections for classification of corrupted images"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "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"

Connected through hyperedge HE-2 (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-3 (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"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"

Connected through hyperedge HE-4 (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"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "gauge equivariant convolutional networks and the icosahedral cnn"):
  with members:
    - "spherical cnns on unstructured grids"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep comprehensive correlation mining for image clustering"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "differentiable linearized admm"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "essential tensor learning for multi view spectral clustering"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-7 (induced by source paper "scattering networks for hybrid representation learning"):
  with members:
    - "generative networks as inverse problems with scattering transforms"
    - "deep unsupervised learning through spatial contrasting"
    - "building a regular decision boundary with deep networks"
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "improved training of wasserstein gans"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-8 (induced by source paper "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "deep clustering for unsupervised learning of visual features"
    - "group equivariant capsule networks"
    - "unsupervised representation learning by predicting image rotations"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"

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)