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: "adversarial learning and explainability in structured datasets"
  Abstract: We theoretically and empirically explore the explainability benefits of adversarial learning in logistic regression models on structured datasets. In particular we focus on improved explainability due to significantly higher $\textit{feature-concentration}$ in adversarially-learned models: Compared to natural training, adversarial training tends to more efficiently shrink the weights of non-predictive and weakly-predictive features, while model performance on natural test data only degrades slightly (and even sometimes improves), compared to that of a naturally trained model. We provide theoretical insight into this phenomenon via an analysis of the expectation of the logistic model weight updates by an SGD-based adversarial learning algorithm, where examples are drawn from a random binary data-generation process. We empirically demonstrate the feature-pruning effect on a synthetic dataset, some datasets from the UCI repository, and real-world large-scale advertising response-prediction data-sets from MediaMath. In several of the MediaMath datasets there are 10s of millions of data points, and on the order of 100,000 sparse categorical features, and adversarial learning often results in model-size reduction by a factor of 20 or higher, and yet the model performance on natural test data (measured by AUC) is comparable to (and sometimes even better than) that of the naturally trained model. We also show that traditional $\ell_1$ regularization does not even come close to achieving this level of feature-concentration. We measure "feature concentration" using the Integrated Gradients-based feature-attribution method of Sundararajan et. al (2017), and derive a new closed-form expression for 1-layer networks, which substantially speeds up computation of aggregate feature attributions across a large dataset.

Connected through hyperedge HE-1 (induced by source paper "ml loo detecting adversarial examples with feature attribution"):
  with members:
    - "on the in fidelity and sensitivity for explanations"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "training verified learners with learned verifiers"
    - "rob gan generator discriminator and adversarial attacker"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "concise and stable explanations using adversarial training"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "attribution driven causal analysis for detection of adversarial examples"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial patch"
    - "concise and stable explanations using adversarial training"
    - "generalization in anti causal learning"
    - "lavan localized and visible adversarial noise"
    - "sanity checks for saliency maps"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "bridging adversarial robustness and gradient interpretability"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "disentangling adversarial robustness and generalization"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "spectral normalization for generative adversarial networks"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "concise and stable explanations using adversarial training"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "robust classification using robust feature augmentation"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "are adversarial examples inevitable"
    - "certified defenses against adversarial examples"
    - "distributionally adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "designing adversarially resilient classifiers using resilient feature engineering"):
  with members:
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "adversarial and clean data are not twins"
    - "robust physical world attacks on machine learning models"
    - "pixeldefend leveraging generative models to understand and defend 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.LG(Machine Learning)