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: "generative adversarial examples"
  Abstract: Adversarial examples are typically constructed by perturbing an existing data point, and current defense methods are focused on guarding against this type of attack. In this paper, we propose a new class of adversarial examples that are synthesized entirely from scratch using a conditional generative model. We first train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to model the class-conditional distribution over inputs. Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier. We demonstrate through human evaluation that this new kind of adversarial inputs, which we call Generative Adversarial Examples, are legitimate and belong to the desired class. Our empirical results on the MNIST, SVHN, and CelebA datasets show that generative adversarial examples can easily bypass strong adversarial training and certified defense methods which can foil existing adversarial attacks.

Connected through hyperedge HE-1 (induced by source paper "on evaluating adversarial robustness"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "the efficacy of shield under different threat models"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "logit pairing methods can fool gradient based attacks"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "disentangling adversarial robustness and generalization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "on the geometry of adversarial examples"
    - "protecting jpeg images against adversarial attacks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adef an iterative algorithm to construct adversarial deformations"
    - "adversarial logit pairing"

Connected through hyperedge HE-3 (induced by source paper "resisting adversarial attacks using gaussian mixture variational autoencoders"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "magnet a two pronged defense against adversarial examples"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "generating natural adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"

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)