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: "max margin adversarial mma training direct input space margin maximization through adversarial training"
  Abstract: We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. In theory, we show that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest successful perturbation". This max-margin perspective also provides an alternative interpretation on adversarial training with a fixed perturbation magnitude $\epsilon$: adversarial training is maximizing either a lower bound or an upper bound of the margin. Motivated by our theoretical analysis, we propose Max-Margin Adversarial (MMA) training to directly maximize the margins. Instead of adversarial training with a fixed $\epsilon$, MMA offers an improvement by selecting the margin as the "correct" $\epsilon$ individually for each point. We demonstrate MMA training's efficacy and analyze its properties on the MNIST and CIFAR10 datasets w.r.t. $\ell_\infty$ and $\ell_2$ robustness.

Connected through hyperedge HE-1 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "sign opt a query efficient hard label adversarial attack"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-2 (induced by source paper "robustness from simple classifiers"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards robust detection of adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-3 (induced by source paper "improved detection of adversarial attacks via penetration distortion maximization"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "stochastic activation pruning for robust adversarial defense"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "deep defense training dnns with improved adversarial robustness"

Connected through hyperedge HE-4 (induced by source paper "on the effectiveness of low frequency perturbations"):
  with members:
    - "are generative classifiers more robust to adversarial attacks"
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "towards the first adversarially robust neural network model on mnist"
    - "generating natural language adversarial examples"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "controlling neural level sets"):
  with members:
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "theoretically principled trade off between robustness and accuracy"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-6 (induced by source paper "advertorch v0 1 an adversarial robustness toolbox based on pytorch"):
  with members:
    - "spatially transformed adversarial examples"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "classification regions of deep neural networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-7 (induced by source paper "adversarial margin maximization networks"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "sok security and privacy in machine learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "aggregated residual transformations for deep neural networks"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "spatially transformed adversarial examples"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial vulnerability for any 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)