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 "hardening deep neural networks via adversarial model cascades"):
  Abstract: Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples - malicious inputs which are crafted by the adversary to induce the trained model to produce erroneous outputs. This vulnerability has inspired a lot of research on how to secure neural networks against these kinds of attacks. Although existing techniques increase the robustness of the models against white-box attacks, they are ineffective against black-box attacks. #R##N#To address the challenge of black-box adversarial attacks, we propose Adversarial Model Cascades (AMC); a framework that performs better than existing state-of-the-art defenses, in both black-box and white-box settings and is easy to integrate into existing set-ups. Our approach trains a cascade of models by injecting images crafted from an already defended proxy model, to improve the robustness of the target models against multiple adversarial attacks simultaneously, in both white-box and black-box settings. We conducted an extensive experimental study to prove the efficiency of our method against multiple attacks; comparing it to numerous defenses, both in white-box and black-box setups.

Includes members:
  Member "stochastic activation pruning for robust adversarial defense":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "exploring the space of black box attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples")
  Member "defense against the dark arts an overview of adversarial example security research and future research directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers")
  Member "certified defenses against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "robust physical world attacks on deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "provable defenses against adversarial examples via the convex outer adversarial polytope":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "practical black box attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)