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: "exploring the space of black box attacks on deep neural networks"
  Abstract: Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models. In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability. We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input. An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs. We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks. We also apply the Gradient Estimation attacks successfully against a real-world content moderation classiﬁer hosted by Clarifai. Furthermore, we evaluate black-box attacks against state-of-the-art defenses. We show that the Gradient Estimation attacks are very effective even against these defenses.

Connected through hyperedge HE-1 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "improving the generalization of adversarial training with domain adaptation"
    - "constructing unrestricted adversarial examples with generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "ape gan adversarial perturbation elimination with gan"
    - "improved training of wasserstein gans"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-2 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples versus cloud based detectors a black box empirical study"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning universal adversarial perturbations with generative models"
    - "google s cloud vision api is not robust to noise"
    - "query efficient black box adversarial examples superceded"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "a method for computing class wise universal adversarial perturbations"):
  with members:
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "black box adversarial attacks with limited queries and information"
    - "certified defenses against adversarial examples"
    - "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"
    - "nag network for adversary generation"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-5 (induced by source paper "hybrid batch attacks finding black box adversarial examples with limited queries"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "there are no bit parts for sign bits in black box attacks"
    - "simple black box adversarial attacks"
    - "stateful detection of black box adversarial attacks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"

Connected through hyperedge HE-6 (induced by source paper "perceptual quality preserving black box attack against deep learning image classifiers"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "attacking convolutional neural network using differential evolution"
    - "black box adversarial attacks with limited queries and information"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "defensive quantization when efficiency meets robustness"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-8 (induced by source paper "at gan a generative attack model for adversarial transferring on generative adversarial nets"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "improving the generalization of adversarial training with domain adaptation"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "ape gan adversarial perturbation elimination with gan"
    - "adversarial transformation networks learning to generate adversarial examples"

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)