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: "minimally distorted adversarial examples with a fast adaptive boundary attack"
  Abstract: The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as the methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $p \in \{1,2,\infty\}$ aiming at finding the minimal perturbation necessary to change the class of a given input. It has an intuitive geometric meaning, yields high quality results already with one restart, minimizes the size of the perturbation, so that the robust accuracy can be evaluated at all possible thresholds with a single run, and comes with almost no free parameters except number of iterations and restarts. It achieves better or similar robust test accuracy compared to state-of-the-art attacks which are partially specialized to one $l_p$-norm.

Connected through hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "robust adversarial perturbation on deep proposal based models"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (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-3 (induced by source paper "output diversified initialization for adversarial attacks"):
  with members:
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "theoretically principled trade off between robustness and accuracy"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "square attack a query efficient black box adversarial attack via random search"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "sparse and imperceivable adversarial attacks"
    - "black box adversarial attacks with bayesian optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "hopskipjumpattack a query efficient decision based attack"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "there are no bit parts for sign bits in black box attacks"

Connected through hyperedge HE-5 (induced by source paper "provable robustness against all adversarial l_p perturbations for p geq 1"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "transfer of adversarial robustness between perturbation types"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial patch"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-6 (induced by source paper "jacobian adversarially regularized networks for robustness"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial examples are a natural consequence of test error in noise"
    - "diagnosing and enhancing vae models"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-7 (induced by source paper "what it thinks is important is important robustness transfers through input gradients"):
  with members:
    - "coco gan generation by parts via conditional coordinating"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "using pre training can improve model robustness and uncertainty"
    - "fixing the train test resolution discrepancy"
    - "adversarially robust transfer learning"

Connected through hyperedge HE-8 (induced by source paper "an alternative surrogate loss for pgd based adversarial testing"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "enhancing gradient based attacks with symbolic intervals"
    - "theoretically principled trade off between robustness and accuracy"
    - "on evaluating adversarial robustness"
    - "feature denoising for improving adversarial robustness"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"
    - "distributionally adversarial attack"

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)