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: "dynamic steerable blocks in deep residual networks"
  Abstract: Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve performance on Cifar-10+ consistently, while having additional pleasant properties like steerability. By exploiting these transformation properties explicitly, we arrive at dynamic steerable blocks. They are an extension of residual blocks, that are able to seamlessly transform filters under pre-defined transformations, conditioned on the input at training and inference time. Dynamic steerable blocks learn the degree of invariance from data and locally adapt filters, allowing them to apply a different geometrical variant of the same filter to each location of the feature map. When evaluated on the Berkeley Segmentation contour detection dataset, our approach outperforms all competing approaches that do not utilize pre-training. Our results highlight the benefits of image-based regularization to deep networks.

Connected through hyperedge HE-1 (induced by source paper "discrete rotation equivariance for point cloud recognition"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-2 (induced by source paper "affine self convolution"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "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"

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 "motion equivariance of event based camera data with the temporal normalization transform"):
  with members:
    - "motion equivariant networks for event cameras with the temporal normalization transform"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "ev segnet semantic segmentation for event based cameras"
    - "asynchronous photometric feature tracking using events and frames"
    - "event based vision meets deep learning on steering prediction for self driving cars"

Connected through hyperedge HE-5 (induced by source paper "a learnable scatternet locally invariant convolutional layers"):
  with members:
    - "deep learning in the wavelet domain"
    - "scaling the scattering transform deep hybrid networks"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "visualizing and improving scattering networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "equivariant transformer networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning compressed transforms with low displacement rank"
    - "sparse unsupervised capsules generalize better"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"

Connected through hyperedge HE-7 (induced by source paper "motion equivariant networks for event cameras with the temporal normalization transform"):
  with members:
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "ev segnet semantic segmentation for event based cameras"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "polar transformer networks"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "learning so 3 equivariant representations with spherical cnns"

Connected through hyperedge HE-8 (induced by source paper "cubenet equivariance to 3d rotation and translation"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"

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)