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: "improved robustness to adversarial examples using lipschitz regularization of the loss"
  Abstract: We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state-of-the-art result in the $\ell_2$ norm on CIFAR-10. We obtain verifiable average case and worst case robustness guarantees, based on the expected and maximum values of the norm of the gradient of the loss. We interpret adversarial training as Total Variation Regularization, which is a fundamental tool in mathematical image processing, and WCAT as Lipschitz regularization.

Connected through hyperedge HE-1 (induced by source paper "interpreting adversarial examples by activation promotion and suppression"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "ml loo detecting adversarial examples with feature attribution"
    - "spatially transformed adversarial examples"
    - "adversarial patch"
    - "interpretable deep learning under fire"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"
    - "lavan localized and visible adversarial noise"

Connected through hyperedge HE-2 (induced by source paper "wasserstein diffusion tikhonov regularization"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "are adversarial examples inevitable"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "deep defense training dnns with improved adversarial robustness"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"

Connected through hyperedge HE-3 (induced by source paper "nonparametric online learning using lipschitz regularized deep neural networks"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "online deep learning learning deep neural networks on the fly"
    - "spectrally normalized margin bounds for neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-4 (induced by source paper "on model robustness against adversarial examples"):
  with members:
    - "mask r cnn"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "identity mappings in deep residual networks"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "robustness of classifiers from adversarial to random noise"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-5 (induced by source paper "the coupling effect of lipschitz regularization in deep neural networks"):
  with members:
    - "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "the power of depth for feedforward neural networks"
    - "adam a method for stochastic optimization"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-6 (induced by source paper "lipschitz regularized deep neural networks generalize and are adversarially robust"):
  with members:
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "spectral normalization for generative adversarial networks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "piecewise strong convexity of neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "understanding deep learning requires rethinking generalization"
    - "identity matters in deep learning"
    - "deep residual learning for image recognition"
    - "on the quality of the initial basin in overspecified neural networks"
    - "the loss surfaces of multilayer networks"

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)