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 rotation equivariant network"
  Abstract: Recently, learning equivariant representations has attracted considerable research attention. Dieleman et al. introduce four operations which can be inserted into convolutional neural network to learn deep representations equivariant to rotation. However, feature maps should be copied and rotated four times in each layer in their approach, which causes much running time and memory overhead. In order to address this problem, we propose Deep Rotation Equivariant Network consisting of cycle layers, isotonic layers and decycle layers. Our proposed layers apply rotation transformation on filters rather than feature maps, achieving a speed up of more than 2 times with even less memory overhead. We evaluate DRENs on Rotated MNIST and CIFAR-10 datasets and demonstrate that it can improve the performance of state-of-the-art architectures.

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 "invariance vs robustness of neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "towards the first adversarially robust neural network model on mnist"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "exploring the landscape of spatial robustness"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning non parametric invariances from data with permanent random connectomes"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "autoaugment learning augmentation policies from data"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "graph based isometry invariant representation learning"
    - "learning to extract motion from videos in convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-4 (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"
    - "visualizing and improving scattering networks"

Connected through hyperedge HE-5 (induced by source paper "non parametric transformation networks"):
  with members:
    - "max margin invariant features from transformed unlabeled data"
    - "dynamic routing between capsules"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "graph based isometry invariant representation learning"
    - "harmonic networks deep translation and rotation equivariance"
    - "learning to extract motion from videos in convolutional neural networks"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "robustness of rotation equivariant networks to adversarial perturbations"):
  with members:
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "spatially transformed adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "learning steerable filters for rotation equivariant cnns"
    - "oriented response networks"
    - "geometric robustness of deep networks analysis and improvement"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in 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)