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: "are accuracy and robustness correlated"
  Abstract: Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the adversarial example generation techniques with respect to the quality of the produced images, and measure the robustness of the tested machine learning models to adversarial examples. Finally, we conduct large-scale experiments on cross-model adversarial portability. We find that adversarial examples are mostly transferable across similar network topologies, and we demonstrate that better machine learning models are less vulnerable to adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-2 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-3 (induced by source paper "effects of differential privacy and data skewness on membership inference vulnerability"):
  with members:
    - "demystifying learning rate polices for high accuracy training of deep neural networks"
    - "cross layer strategic ensemble defense against adversarial examples"
    - "differentially private model publishing for deep learning"
    - "evaluating differentially private machine learning in practice"
    - "robust or private adversarial training makes models more vulnerable to privacy attacks"
    - "deep neural network ensembles against deception ensemble diversity accuracy and robustness"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-4 (induced by source paper "adversarial risk bounds for neural networks through sparsity based compression"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"
    - "generalizable adversarial training via spectral normalization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-5 (induced by source paper "on the effect of low rank weights on adversarial robustness of neural networks"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "sparse dnns with improved adversarial robustness"
    - "on generation of adversarial examples using convex programming"
    - "low rank structure of learned representations"
    - "opening the black box of deep neural networks via information"
    - "a survey on deep learning in medical image analysis"
    - "estimating mixture entropy with pairwise distances"
    - "nonlinear information bottleneck"

Connected through hyperedge HE-6 (induced by source paper "disentangling adversarial robustness and generalization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "generative adversarial examples"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "detection based defense against adversarial examples from the steganalysis point of view"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "enhancing robustness of machine learning systems via data transformations"
    - "adversarial and clean data are not twins"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "towards demystifying membership inference attacks"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "understanding membership inferences on well generalized learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "towards measuring membership privacy"
    - "logan membership inference attacks against generative models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "adversarial examples attacks and defenses for deep learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

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.CV(Computer Vision and Pattern Recognition)