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: "scale equivariance in cnns with vector fields"
  Abstract: We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a vector field encoding for the maximally activating scale and the scale itself, which is further processed by the following convolutional layers. This allows all the intermediate representations to be locally scale equivariant. We show that this improves the performance of the model by over 20% in the scale equivariant task of regressing the scaling factor applied to randomly scaled MNIST digits. Furthermore, we find it also useful for scale invariant tasks, such as the actual classification of randomly scaled digits. This highlights the usefulness of allowing for a compact representation that can also learn relationships between different local scales by keeping internal scale equivariance.

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 "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-3 (induced by source paper "scale equivariant steerable networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep scale spaces equivariance over scale"):
  with members:
    - "roto translation covariant convolutional networks for medical image analysis"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "rotation equivariant cnns for digital pathology"
    - "learning steerable filters for rotation equivariant cnns"
    - "dilated residual networks"
    - "learning so 3 equivariant representations with spherical cnns"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "scale equivariant neural networks with decomposed convolutional filters"):
  with members:
    - "selective kernel networks"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "learning steerable filters for rotation equivariant cnns"
    - "oriented response networks"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "co attentive equivariant neural networks focusing equivariance on transformations co occurring in data"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "cubenet equivariance to 3d rotation and translation"
    - "cbam convolutional block attention module"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "attention is all you need"
    - "harmonic networks deep translation and rotation equivariance"

Connected through hyperedge HE-7 (induced by source paper "scale steerable filters for locally scale invariant convolutional neural networks"):
  with members:
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "group equivariant convolutional networks"
    - "spatial transformer networks"
    - "locally scale invariant convolutional neural 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)