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: "cubenet equivariance to 3d rotation and translation"
  Abstract: 3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. #R##N#We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.

Connected through hyperedge HE-1 (induced by source paper "on learning sets of symmetric elements"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "on the limitations of representing functions on sets"
    - "permutation invariant feature restructuring for correlation aware image set based recognition"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "set2graph learning graphs from sets"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "meta learning to cluster"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on universal equivariant set networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "clustering with deep learning taxonomy and new methods"

Connected through hyperedge HE-4 (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"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "3d g cnns for pulmonary nodule detection"
    - "computing cnn loss and gradients for pose estimation with riemannian geometry"
    - "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-5 (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"
    - "roto translation covariant convolutional networks for medical image analysis"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "rotational 3d texture classification using group equivariant cnns"
    - "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-8 (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"

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)