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 "generalizable adversarial examples detection based on bi model decision mismatch"):
  Abstract: Deep neural networks (DNNs) have shown phenomenal success in a wide range of applications. However, recent studies have discovered that they are vulnerable to Adversarial Examples, i.e., original samples with added subtle perturbations. Such perturbations are often too small and imperceptible to humans, yet they can easily fool the neural networks. Few defense techniques against adversarial examples have been proposed, but they require modifying the target model or prior knowledge of adversarial examples generation methods. Likewise, their performance remarkably drops upon encountering adversarial example types not used during the training stage. In this paper, we propose a new framework that can be used to enhance DNNs' robustness by detecting adversarial examples. In particular, we employ the decision layer of independently trained models as features for posterior detection. The proposed framework doesn't require any prior knowledge of adversarial examples generation techniques, and can be directly augmented with unmodified off-the-shelf models. Experiments on the standard MNIST and CIFAR10 datasets show that it generalizes well across not only different adversarial examples generation methods but also various additive perturbations. Specifically, distinct binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain this performance when tested against unseen attacks.

Includes members:
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "training ensembles to detect adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lower bounds on adversarial robustness from optimal transport")
  Member "on the statistical detection of adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "delving into transferable adversarial examples and black box attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial examples in the physical world":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "distillation as a defense to adversarial perturbations against deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.CV(Computer Vision and Pattern Recognition)