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: "universal decision based black box perturbations breaking security through obscurity defenses"
  Abstract: We study the problem of finding a universal (image-agnostic) perturbation to fool machine learning (ML) classifiers (e.g., neural nets, decision tress) in the hard-label black-box setting. Recent work in adversarial ML in the white-box setting (model parameters are known) has shown that many state-of-the-art image classifiers are vulnerable to universal adversarial perturbations: a fixed human-imperceptible perturbation that, when added to any image, causes it to be misclassified with high probability Kurakin et al. [2016], Szegedy et al. [2013], Chen et al. [2017a], Carlini and Wagner [2017]. This paper considers a more practical and challenging problem of finding such universal perturbations in an obscure (or black-box) setting. More specifically, we use zeroth order optimization algorithms to find such a universal adversarial perturbation when no model information is revealed-except that the attacker can make queries to probe the classifier. We further relax the assumption that the output of a query is continuous valued confidence scores for all the classes and consider the case where the output is a hard-label decision. Surprisingly, we found that even in these extremely obscure regimes, state-of-the-art ML classifiers can be fooled with a very high probability just by adding a single human-imperceptible image perturbation to any natural image. The surprising existence of universal perturbations in a hard-label black-box setting raises serious security concerns with the existence of a universal noise vector that adversaries can possibly exploit to break a classifier on most natural images.

Connected through hyperedge HE-1 (induced by source paper "towards an efficient and general framework of robust training for graph neural networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "zo adamm zeroth order adaptive momentum method for black box optimization"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adversarial attack on graph structured data"
    - "zeroth order stochastic variance reduction for nonconvex optimization"

Connected through hyperedge HE-2 (induced by source paper "mimicgan robust projection onto image manifolds with corruption mimicking"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "nam non adversarial unsupervised domain mapping"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-3 (induced by source paper "generative counterfactual introspection for explainable deep learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "counterfactual visual explanations"
    - "grounding visual explanations"
    - "explaining deep learning models using causal inference"
    - "attgan facial attribute editing by only changing what you want"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-4 (induced by source paper "mimicgan corruption mimicking for blind image recovery adversarial defense"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "the robust manifold defense adversarial training using generative models"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "lose the views limited angle ct reconstruction via implicit sinogram completion"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "photo realistic single image super resolution using a generative adversarial network"

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)