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: "quantifying translation invariance in convolutional neural networks"
  Abstract: A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple a tool, the translation-sensitivity map, which we use to visualize and quantify the translation-invariance of various architectures. We obtain the surprising result that architectural choices such as the number of pooling layers and the convolution filter size have only a secondary effect on the translation-invariance of a network. Our analysis identifies training data augmentation as the most important factor in obtaining translation-invariant representations of images using convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "evading defenses to transferable adversarial examples by translation invariant attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "improving transferability of adversarial examples with input diversity"
    - "max mahalanobis linear discriminant analysis networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "towards robust detection of adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "locally connected spiking neural networks for unsupervised feature learning"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp learning of image patches with convolutional spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "unsupervised learning with self organizing spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient computation in adaptive artificial spiking neural networks"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"

Connected through hyperedge HE-3 (induced by source paper "why do deep convolutional networks generalize so poorly to small image transformations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "studying invariances of trained convolutional neural networks"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "exploring the space of black box attacks on deep neural networks"
    - "the elephant in the room"

Connected through hyperedge HE-4 (induced by source paper "the effect of learning strategy versus inherent architecture properties on the ability of convolutional neural networks to develop transformation invariance"):
  with members:
    - "patch reordering a novel way to achieve rotation and translation invariance in convolutional neural networks"
    - "studying invariances of trained convolutional neural networks"
    - "dynamic routing between capsules"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "group equivariant convolutional networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

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)