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: "learning adversary resistant deep neural networks"
  Abstract: Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design. This attack consists of generating particular synthetic examples referred to as adversarial samples. These samples are constructed by slightly manipulating real data-points in order to "fool" the original DNN model, forcing it to mis-classify previously correctly classified samples with high confidence. Addressing this flaw in the model is essential if DNNs are to be used in critical applications such as those in cyber security. #R##N#Previous work has provided various learning algorithms to enhance the robustness of DNN models, and they all fall into the tactic of "security through obscurity". This means security can be guaranteed only if one can obscure the learning algorithms from adversaries. Once the learning technique is disclosed, DNNs protected by these defense mechanisms are still susceptible to adversarial samples. In this work, we investigate this issue shared across previous research work and propose a generic approach to escalate a DNN's resistance to adversarial samples. More specifically, our approach integrates a data transformation module with a DNN, making it robust even if we reveal the underlying learning algorithm. To demonstrate the generality of our proposed approach and its potential for handling cyber security applications, we evaluate our method and several other existing solutions on datasets publicly available. Our results indicate that our approach typically provides superior classification performance and resistance in comparison with state-of-art solutions.

Connected through hyperedge HE-1 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial attacks and defences a survey"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "moving target defense for deep visual sensing against adversarial examples"):
  with members:
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-3 (induced by source paper "detecting adversarial image examples in deep neural networks with adaptive noise reduction"):
  with members:
    - "deep text classification can be fooled"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "enhancing robustness of machine learning systems via data transformations"
    - "adversarial and clean data are not twins"
    - "on the statistical detection of adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-4 (induced by source paper "adversarial feature genome a data driven adversarial examples recognition method"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "high dimensional spaces deep learning and adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial perturbations against real time video classification systems"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "fast geometrically perturbed adversarial faces"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "spatially transformed adversarial examples"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "safer classification by synthesis"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "gradient band based adversarial training for generalized attack immunity of a3c path finding"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards proving the adversarial robustness of deep neural networks"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "extending defensive distillation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "defense against universal adversarial perturbations"

Connected through hyperedge HE-7 (induced by source paper "a data driven adversarial examples recognition framework via adversarial feature genome"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial perturbations against real time video classification systems"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-8 (induced by source paper "adversarial defense based on structure to signal autoencoders"):
  with members:
    - "adversarial patch"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "delving into transferable adversarial examples and black box attacks"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "the new data and new challenges in multimedia research"
    - "very deep convolutional networks for large scale image recognition"
    - "intriguing properties of neural networks"

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)