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: "gauge equivariant convolutional networks and the icosahedral cnn"
  Abstract: The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture design. Equivariant networks have shown excellent performance and data efficiency on vision and medical imaging problems that exhibit symmetries. Here we show how this principle can be extended beyond global symmetries to local gauge transformations. This enables the development of a very general class of convolutional neural networks on manifolds that depend only on the intrinsic geometry, and which includes many popular methods from equivariant and geometric deep learning. We implement gauge equivariant CNNs for signals defined on the surface of the icosahedron, which provides a reasonable approximation of the sphere. By choosing to work with this very regular manifold, we are able to implement the gauge equivariant convolution using a single conv2d call, making it a highly scalable and practical alternative to Spherical CNNs. Using this method, we demonstrate substantial improvements over previous methods on the task of segmenting omnidirectional images and global climate patterns.

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"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"
    - "a simple proof of the universality of invariant equivariant graph neural 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"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "attention augmented convolutional networks"
    - "bam bottleneck attention module"

Connected through hyperedge HE-3 (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"
    - "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"
    - "a general theory of equivariant cnns on homogeneous spaces"

Connected through hyperedge HE-4 (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"
    - "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"
    - "multi directional geodesic neural networks via equivariant convolution"

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"
    - "equivariant neural networks and equivarification"
    - "invariant representations without adversarial training"
    - "neural ordinary differential equations"
    - "learning steerable filters for rotation equivariant cnns"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "universal equivariant multilayer perceptrons"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "on the universality of invariant networks"
    - "on universal equivariant set networks"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-7 (induced by source paper "directional message passing for molecular graphs"):
  with members:
    - "on the convergence of adam and beyond"
    - "provably powerful graph networks"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep 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-8 (induced by source paper "stand alone self attention in vision models"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "local relation networks for image recognition"
    - "nas fpn learning scalable feature pyramid architecture for object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "self attention with relative position representations"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)