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: "defending against whitebox adversarial attacks via randomized discretization"
  Abstract: Adversarial perturbations dramatically decrease the accuracy of state-of-the-art image classifiers. In this paper, we propose and analyze a simple and computationally efficient defense strategy: inject random Gaussian noise, discretize each pixel, and then feed the result into any pre-trained classifier. Theoretically, we show that our randomized discretization strategy reduces the KL divergence between original and adversarial inputs, leading to a lower bound on the classification accuracy of any classifier against any (potentially whitebox) $\ell_\infty$-bounded adversarial attack. Empirically, we evaluate our defense on adversarial examples generated by a strong iterative PGD attack. On ImageNet, our defense is more robust than adversarially-trained networks and the winning defenses of the NIPS 2017 Adversarial Attacks & Defenses competition.

Connected through hyperedge HE-1 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-2 (induced by source paper "colored noise injection for training adversarially robust neural networks"):
  with members:
    - "training robust deep neural networks via adversarial noise propagation"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "adversarial training with voronoi constraints"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning to defense by learning to attack"
    - "efficient defenses against adversarial attacks"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-3 (induced by source paper "impact of low bitwidth quantization on the adversarial robustness for embedded neural networks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "on evaluating adversarial robustness"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "stochastic activation pruning for robust adversarial defense"
    - "efficient two step adversarial defense for deep neural networks"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-4 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  with members:
    - "discretization based solutions for secure machine learning against adversarial attacks"
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "weight map layer for noise and adversarial attack robustness"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "simple black box adversarial attacks"

Connected through hyperedge HE-5 (induced by source paper "smoothed inference for adversarially trained models"):
  with members:
    - "robust unsupervised neural machine translation with adversarial training"
    - "maxup a simple way to improve generalization of neural network training"
    - "randomized smoothing of all shapes and sizes"
    - "colored noise injection for training adversarially robust neural networks"
    - "enforcing linearity in dnn succours robustness and adversarial image generation"
    - "training robust deep neural networks via adversarial noise propagation"
    - "adversarial policies attacking deep reinforcement learning"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"

Connected through hyperedge HE-6 (induced by source paper "extracting robust and accurate features via a robust information bottleneck"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "unifying the brascamp lieb inequality and the entropy power inequality"
    - "where is the information in a deep neural network"
    - "a family of bayesian cram er rao bounds and consequences for log concave priors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "does learning require memorization a short tale about a long tail"
    - "a spectral view of adversarially robust features"

Connected through hyperedge HE-7 (induced by source paper "are perceptually aligned gradients a general property of robust classifiers"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certifiably robust interpretation in deep learning"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "theoretical evidence for adversarial robustness through randomization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards robust neural networks via random self ensemble"
    - "mitigating evasion attacks to deep neural networks via region based classification"
    - "improving the robustness of deep neural networks via stability training"

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.LG(Machine Learning)