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: "hardening deep neural networks via adversarial model cascades"
  Abstract: Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples - malicious inputs which are crafted by the adversary to induce the trained model to produce erroneous outputs. This vulnerability has inspired a lot of research on how to secure neural networks against these kinds of attacks. Although existing techniques increase the robustness of the models against white-box attacks, they are ineffective against black-box attacks. #R##N#To address the challenge of black-box adversarial attacks, we propose Adversarial Model Cascades (AMC); a framework that performs better than existing state-of-the-art defenses, in both black-box and white-box settings and is easy to integrate into existing set-ups. Our approach trains a cascade of models by injecting images crafted from an already defended proxy model, to improve the robustness of the target models against multiple adversarial attacks simultaneously, in both white-box and black-box settings. We conducted an extensive experimental study to prove the efficiency of our method against multiple attacks; comparing it to numerous defenses, both in white-box and black-box setups.

Connected through hyperedge HE-1 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial training for free"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial perturbations on the perceptual ball"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "interpretable counterfactual explanations guided by prototypes"
    - "universal adversarial perturbations against person re identification"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "counterfactual visual explanations"
    - "disentangling adversarial robustness and generalization"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial perturbations"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "blind pre processing a robust defense method against adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "exploring the space of black box attacks on deep neural networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "certified defenses 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)