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: "square attack a query efficient black box adversarial attack via random search"
  Abstract: We propose the Square Attack, a new score-based black-box $l_2$ and $l_\infty$ adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where we select localized square-shaped updates at random positions so that the $l_\infty$- or $l_2$-norm of the perturbation is approximately equal to the maximal budget at each step. Our method is algorithmically transparent, robust to the choice of hyperparameters, and is significantly more query efficient compared to the more complex state-of-the-art methods. In particular, on ImageNet we improve the average query efficiency for various deep networks by a factor of at least $2$ and up to $7$ compared to the recent state-of-the-art $l_\infty$-attack of Meunier et al. while having a higher success rate. The Square Attack can even be competitive to gradient-based white-box attacks in terms of success rate. Moreover, we show its utility by breaking a recently proposed defense based on randomization. The code of our attack is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "controlling neural level sets"
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "jacobian adversarially regularized networks for robustness"

Connected through hyperedge HE-2 (induced by source paper "a model based derivative free approach to black box adversarial examples bobyqa"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "fake news detection on social media using geometric deep learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "darts deceiving autonomous cars with toxic signs"
    - "low frequency adversarial perturbation"
    - "robust physical world attacks on deep learning models"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-3 (induced by source paper "confidence calibrated adversarial training generalizing to unseen attacks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial robustness against the union of multiple perturbation models"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "playing the game of universal adversarial perturbations"

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)