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: "on the limitation of magnet defense against l_1 based adversarial examples"
  Abstract: In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security. In particular, MagNet consisting of an adversary detector and a data reformer is by far one of the strongest defenses in the black-box oblivious attack setting, where the attacker aims to craft transferable adversarial examples from an undefended DNN model to bypass an unknown defense module deployed on the same DNN model. Under this setting, MagNet can successfully defend a variety of attacks in DNNs, including the high-confidence adversarial examples generated by the Carlini and Wagner's attack based on the $L_2$ distortion metric. However, in this paper, under the same attack setting we show that adversarial examples crafted based on the $L_1$ distortion metric can easily bypass MagNet and mislead the target DNN image classifiers on MNIST and CIFAR-10. We also provide explanations on why the considered approach can yield adversarial examples with superior attack performance and conduct extensive experiments on variants of MagNet to verify its lack of robustness to $L_1$ distortion based attacks. Notably, our results substantially weaken the assumption of effective threat models on MagNet that require knowing the deployed defense technique when attacking DNNs (i.e., the gray-box attack setting).

Connected through hyperedge HE-1 (induced by source paper "raid randomized adversarial input detection for neural networks"):
  with members:
    - "when explainability meets adversarial learning detecting adversarial examples using shap signatures"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "deepsmartfuzzer reward guided test generation for deep learning"
    - "deepfault fault localization for deep neural networks"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-2 (induced by source paper "deepsearch simple and effective blackbox fuzzing of deep neural networks"):
  with members:
    - "towards improved testing for deep learning"
    - "algorithms for verifying deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "on evaluating adversarial robustness"
    - "the efficacy of shield under different threat models"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "symbolic execution for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "on evaluating adversarial robustness"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "the efficacy of shield under different threat models"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "generative adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-4 (induced by source paper "scratch that an evolution based adversarial attack against neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "they might not be giants crafting black box adversarial examples with fewer queries using particle swarm optimization"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial patch"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "genattack practical black box attacks with gradient free optimization"

Connected through hyperedge HE-5 (induced by source paper "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "spatially transformed adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "query efficient hard label black box attack an optimization based approach"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-6 (induced by source paper "characterizing audio adversarial examples using temporal dependency"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "fooling end to end speaker verification by adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "genattack practical black box attacks with gradient free optimization"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adversarial attacks and defences competition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "towards the first adversarially robust neural network model on mnist"
    - "generating natural language adversarial examples"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "black box adversarial attacks with limited queries and information"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "on the utility of conditional generation based mutual information for characterizing adversarial subspaces"):
  with members:
    - "alphagan generative adversarial networks for natural image matting"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against 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.CV(Computer Vision and Pattern Recognition)