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: "simple black box adversarial attacks"
  Abstract: We propose an intriguingly simple method for the construction of adversarial images in the black-box setting. In constrast to the white-box scenario, constructing black-box adversarial images has the additional constraint on query budget, and efficient attacks remain an open problem to date. With only the mild assumption of continuous-valued confidence scores, our highly query-efficient algorithm utilizes the following simple iterative principle: we randomly sample a vector from a predefined orthonormal basis and either add or subtract it to the target image. Despite its simplicity, the proposed method can be used for both untargeted and targeted attacks -- resulting in previously unprecedented query efficiency in both settings. We demonstrate the efficacy and efficiency of our algorithm on several real world settings including the Google Cloud Vision API. We argue that our proposed algorithm should serve as a strong baseline for future black-box attacks, in particular because it is extremely fast and its implementation requires less than 20 lines of PyTorch code.

Connected through hyperedge HE-1 (induced by source paper "black box smoothing a provable defense for pretrained classifiers"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-2 (induced by source paper "a survey of black box adversarial attacks on computer vision models"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "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"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-3 (induced by source paper "ensembles of many diverse weak defenses can be strong defending deep neural networks against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "enhancing robustness of machine learning systems via data transformations"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "adversarial vertex mixup toward better adversarially robust generalization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "theoretically principled trade off between robustness and accuracy"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"

Connected through hyperedge HE-5 (induced by source paper "frequency tuned universal adversarial attacks"):
  with members:
    - "a survey of black box adversarial attacks on computer vision models"
    - "on the effectiveness of low frequency perturbations"
    - "a geometry inspired decision based attack"
    - "defending against universal attacks through selective feature regeneration"
    - "a study of black box adversarial attacks in computer vision"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "high frequency component helps explain the generalization of convolutional neural networks"
    - "universal adversarial training"

Connected through hyperedge HE-6 (induced by source paper "detecting and recovering adversarial examples an input sensitivity guided method"):
  with members:
    - "gradient similarity an explainable approach to detect adversarial attacks against deep learning"
    - "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"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"
    - "magnet a two pronged defense against adversarial examples"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-7 (induced by source paper "geoda a geometric framework for black box adversarial attacks"):
  with members:
    - "a geometry inspired decision based attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "stateful detection of black box adversarial attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "simple black box adversarial perturbations for deep networks"

Connected through hyperedge HE-8 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  with members:
    - "discretization based solutions for secure machine learning against adversarial attacks"
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "weight map layer for noise and adversarial attack robustness"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "gandef a gan based adversarial training defense for neural network classifier"

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)