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 defense via data dependent activation function and total variation minimization"
  Abstract: We improve the robustness of deep neural nets to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation remarkably improves both generalization and robustness towards adversarial attacks. In the CIFAR10 benchmark, we raise the accuracy of the Projected Gradient Descent adversarial training from $\sim 46\%$ to $\sim 69\%$ for ResNet20. When we combine this data-dependent activation with total variation minimization on adversarial images and training data augmentation, we achieve an improvement in accuracy by 38.9$\%$ for ResNet56 under the strongest attack of the Iterative Fast Gradient Sign Method. We further provide an intuitive explanation of our defense by analyzing the geometry of the feature space. For reproducibility, the code is made available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "graph interpolating activation improves both natural and robust accuracies in data efficient deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "mathematical analysis of adversarial attacks"
    - "deep neural nets with interpolating function as output activation"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "laplacian smoothing gradient descent"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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"
    - "improved robustness to adversarial examples using lipschitz regularization of the loss"
    - "are adversarial examples inevitable"
    - "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 "on robustness of neural ordinary differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "accelerating neural odes with spectral elements"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "analyzing inverse problems with invertible neural networks"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deep neural nets with interpolating function as output activation"
    - "neural ordinary differential equations"

Connected through hyperedge HE-4 (induced by source paper "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "mathematical analysis of adversarial attacks"
    - "deep neural nets with interpolating function as output activation"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "mathematical analysis of adversarial attacks"):
  with members:
    - "deep learning with data dependent implicit activation function"
    - "deep neural nets with interpolating function as output activation"
    - "black box adversarial attacks with limited queries and information"
    - "sok security and privacy in machine learning"
    - "laplacian smoothing gradient descent"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "synthesizing robust 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)