human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "output diversified initialization for adversarial attacks"):
  Abstract: Adversarial examples are often constructed by iteratively refining a randomly perturbed input. To improve diversity and thus also the success rates of attacks, we propose Output Diversified Initialization (ODI), a novel random initialization strategy that can be combined with most existing white-box adversarial attacks. Instead of using uniform perturbations in the input space, we seek diversity in the output logits space of the target model. Empirically, we demonstrate that existing $\ell_\infty$ and $\ell_2$ adversarial attacks with ODI become much more efficient on several datasets including MNIST, CIFAR-10 and ImageNet, reducing the accuracy of recently proposed defense models by 1--17\%. Moreover, PGD attack with ODI outperforms current state-of-the-art attacks against robust models, while also being roughly 50 times faster on CIFAR-10. The code is available on this https URL.

Includes members:
  Member "metric learning for adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "an alternative surrogate loss for pgd based adversarial testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on adaptive attacks to adversarial example defenses")
  Member "theoretically principled trade off between robustness and accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "defense against adversarial attacks using feature scattering based adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "minimally distorted adversarial examples with a fast adaptive boundary attack":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "feature denoising for improving adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "robustness via curvature regularization and vice versa":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "varmixup exploiting the latent space for robust training and inference")

Question: Please tell me which class the source paper 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)