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: "learning steerable filters for rotation equivariant cnns"
  Abstract: In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs) which achieve joint equivariance under translations and rotations by design. The proposed architecture employs steerable filters to efficiently compute orientation dependent responses for many orientations without suffering interpolation artifacts from filter rotation. We utilize group convolutions which guarantee an equivariant mapping. In addition, we generalize He's weight initialization scheme to filters which are defined as a linear combination of a system of atomic filters. Numerical experiments show a substantial enhancement of the sample complexity with a growing number of sampled filter orientations and confirm that the network generalizes learned patterns over orientations. The proposed approach achieves state-of-the-art on the rotated MNIST benchmark and on the ISBI 2012 2D EM segmentation challenge.

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"
    - "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"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

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"
    - "roto translation covariant convolutional networks for medical image analysis"

Connected through hyperedge HE-4 (induced by source paper "rsl net localising in satellite images from a radar on the ground"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "conditional image to image translation"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "polar transformer networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"

Connected through hyperedge HE-5 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "invariant representations through adversarial forgetting"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant neural networks and equivarification"
    - "invariant representations without adversarial training"
    - "neural ordinary differential equations"
    - "decomposing motion and content for natural video sequence prediction"

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"
    - "cubenet equivariance to 3d rotation and translation"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "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"
    - "cubenet equivariance to 3d rotation and translation"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "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.LG(Machine Learning)