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 scale spaces equivariance over scale"
  Abstract: We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainly, the class of an image is invariant to the scale at which it is viewed. We construct scale equivariant cross-correlations based on a principled extension of convolutions, grounded in the theory of scale-spaces and semigroups. As a very basic operation, these cross-correlations can be used in almost any modern deep learning architecture in a plug-and-play manner. We demonstrate our networks on the Patch Camelyon and Cityscapes datasets, to prove their utility and perform introspective studies to further understand their properties.

Connected through hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks"):
  with members:
    - "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"
    - "bam bottleneck attention module"

Connected through hyperedge HE-2 (induced by source paper "roto translation equivariant convolutional networks application to histopathology image analysis"):
  with members:
    - "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"
    - "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:
    - "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"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-4 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization"):
  with members:
    - "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"
    - "learning steerable filters for rotation equivariant cnns"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-5 (induced by source paper "b spline cnns on lie groups"):
  with members:
    - "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"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "affine self convolution"):
  with members:
    - "equivariant multi view networks"
    - "stand alone self attention in vision models"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "attention augmented convolutional networks"
    - "local relation networks for image recognition"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-7 (induced by source paper "self supervised scale equivariant network for weakly supervised semantic segmentation"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "unsupervised representation learning by predicting image rotations"
    - "self erasing network for integral object attention"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-8 (induced by source paper "scale equivariant steerable networks"):
  with members:
    - "scale steerable filters for locally scale invariant convolutional neural networks"
    - "harmonic networks with limited training samples"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant transformer networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "scale equivariance in cnns with vector fields"

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)