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: "concise and stable explanations using adversarial training"
  Abstract: We show new connections between adversarial learning and explainability. One form of explanation of the output of a neural network model in terms of its input features, is a vector of feature-attributions using the Integrated Gradient (IG) method. Two desirable characteristics of an attribution-based explanation are: (1)$\mathit{sparseness}$: the attributions of irrelevant or weakly relevant features should be negligible, thus resulting in $\mathit{concise}$ explanations in terms of the significant features, and (2) $\mathit{stability}$: it should not vary significantly within a small local neighborhood of the input. For a general class of convex loss functions, we show two new theoretical results: (a) via a novel analysis of the Stochastic Gradient Descent process, we show that adversarial training using an $\ell_\infty$-bounded adversary produces sparse models (and hence concise explanations), and (b) we show that natural model-training while encouraging stable IG-explanations (via an extra term in the loss function), is equivalent to adversarial training. We demonstrate the first phenomenon in experiments training logistic regression models on a variety of tabular toy and real-world advertising datasets: adversarial training yields significantly sparser models, with little or no degradation in performance on natural test data.

Connected through hyperedge HE-1 (induced by source paper "heat and blur an effective and fast defense against adversarial examples"):
  with members:
    - "concise explanations of neural networks using adversarial training"
    - "adversarial examples are a natural consequence of test error in noise"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "saliency methods for explaining adversarial attacks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "spatially transformed adversarial examples"
    - "interpretable deep learning under fire"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-2 (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"
    - "adversarial learning and explainability in structured datasets"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"

Connected through hyperedge HE-3 (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"
    - "adversarial learning and explainability in structured datasets"
    - "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-4 (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"
    - "adversarial learning and explainability in structured datasets"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "sanity checks for saliency maps"

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)