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: "robustness of rotation equivariant networks to adversarial perturbations"
  Abstract: Deep neural networks have been shown to be vulnerable to adversarial examples: very small perturbations of the input having a dramatic impact on the predictions. A wealth of adversarial attacks and distance metrics to quantify the similarity between natural and adversarial images have been proposed, recently enlarging the scope of adversarial examples with geometric transformations beyond pixel-wise attacks. In this context, we investigate the robustness to adversarial attacks of new Convolutional Neural Network architectures providing equivariance to rotations. We found that rotation-equivariant networks are significantly less vulnerable to geometric-based attacks than regular networks on the MNIST, CIFAR-10, and ImageNet datasets.

Connected through hyperedge HE-1 (induced by source paper "recurrent attention model with log polar mapping is robust against adversarial attacks"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "spatially transformed adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "polar transformer networks"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"):
  with members:
    - "equivariant transformer networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "unsupervised data augmentation for consistency training"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generalisation in humans and deep neural networks"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"

Connected through hyperedge HE-3 (induced by source paper "perturbations are not enough generating adversarial examples with spatial distortions"):
  with members:
    - "spatially transformed adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "rob gan generator discriminator and adversarial attacker"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "improved network robustness with adversary critic"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "machine vs machine minimax optimal defense against adversarial examples"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "bidirectional learning for robust neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural networks with transformed input based on robust tensor network decomposition"):
  with members:
    - "interpretable convolutional neural networks via feedforward design"
    - "spatially transformed adversarial examples"
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "on data driven saak transform"
    - "expressive power of recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "rotation equivariant cnns for digital pathology"):
  with members:
    - "learning steerable filters for rotation equivariant cnns"
    - "detecting cancer metastases on gigapixel pathology images"
    - "a survey on deep learning in medical image analysis"
    - "harmonic networks deep translation and rotation equivariance"
    - "densely connected convolutional networks"
    - "group equivariant convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "rotational 3d texture classification using group equivariant cnns"):
  with members:
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "learning steerable filters for rotation equivariant cnns"
    - "harmonic networks deep translation and rotation equivariance"
    - "deep ten texture encoding network"
    - "group equivariant convolutional networks"
    - "imagenet large scale visual recognition challenge"

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)