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: "defense against the dark arts an overview of adversarial example security research and future research directions"
  Abstract: This article presents a summary of a keynote lecture at the Deep Learning Security workshop at IEEE Security and Privacy 2018. This lecture summarizes the state of the art in defenses against adversarial examples and provides recommendations for future research directions on this topic.

Connected through hyperedge HE-1 (induced by source paper "semantic adversarial attacks parametric transformations that fool deep classifiers"):
  with members:
    - "spatially transformed adversarial examples"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robustness via curvature regularization and vice versa"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial vulnerability for any classifier"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "targeted nonlinear adversarial perturbations in images and videos"

Connected through hyperedge HE-2 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-3 (induced by source paper "procedural noise adversarial examples for black box attacks on deep convolutional networks"):
  with members:
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generating adversarial examples with adversarial networks"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"

Connected through hyperedge HE-4 (induced by source paper "collaborative sampling in generative adversarial networks"):
  with members:
    - "collagan collaborative gan for missing image data imputation"
    - "theoretically principled trade off between robustness and accuracy"
    - "generative collaborative networks for single image super resolution"
    - "self supervised gans via auxiliary rotation loss"
    - "phase collaborative network for multi phase medical imaging segmentation"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "adversarial feedback loop"

Connected through hyperedge HE-5 (induced by source paper "deep neural network ensembles against deception ensemble diversity accuracy and robustness"):
  with members:
    - "denoising and verification cross layer ensemble against black box adversarial attacks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial examples in deep learning characterization and divergence"
    - "learning transferable adversarial examples via ghost networks"
    - "magnet a two pronged defense against adversarial examples"
    - "training ensembles to detect adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-6 (induced by source paper "cross layer strategic ensemble defense against adversarial examples"):
  with members:
    - "deep neural network ensembles against deception ensemble diversity accuracy and robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "adversarial examples in deep learning characterization and divergence"
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "mitigating adversarial effects through randomization"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-7 (induced by source paper "the efficacy of shield under different threat models"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial patch"
    - "black box adversarial attacks with limited queries and information"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-8 (induced by source paper "requisite variety in ethical utility functions for ai value alignment"):
  with members:
    - "augmented utilitarianism for agi safety"
    - "scalable agent alignment via reward modeling a research direction"
    - "impossibility and uncertainty theorems in ai value alignment or why your agi should not have a utility function"

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)