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: "l2 nonexpansive neural networks"
  Abstract: This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness: our linear and convolution layers subsume those in the previous Parseval networks as a special case and allow greater degrees of freedom; aggregation, pooling, splitting and other operators are adapted in new ways, and a new loss function is proposed, all for the purpose of improving robustness. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. Their outputs are quantitatively more meaningful than ordinary networks and indicate levels of confidence. They are also free of exploding gradients, among other desirable properties.

Connected through hyperedge HE-1 (induced by source paper "on evaluating adversarial robustness"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "the efficacy of shield under different threat models"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "generative adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-2 (induced by source paper "adversarial training for free"):
  with members:
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "stochastic activation pruning for robust adversarial defense"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-3 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-4 (induced by source paper "preventing gradient attenuation in lipschitz constrained convolutional networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "provable robustness against all adversarial l_p perturbations for p geq 1"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "sorting out lipschitz function approximation"
    - "limitations of the lipschitz constant as a defense against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "l relu piece wise linear activation functions for deep fine grained visual categorization"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "multi attention multi class constraint for fine grained image recognition"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "self normalizing neural networks"
    - "searching for activation functions"

Connected through hyperedge HE-6 (induced by source paper "defensive quantization when efficiency meets robustness"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "defend deep neural networks against adversarial examples via fixed anddynamic quantized activation functions"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "defending against adversarial examples with k nearest neighbor"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial training with voronoi constraints"
    - "sorting out lipschitz function approximation"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-8 (induced by source paper "testing robustness against unforeseen adversaries"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "sensitivity of deep convolutional networks to gabor noise"
    - "spatially transformed adversarial examples"

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.AI(Artificial Intelligence)