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: "trust region based adversarial attack on neural networks"
  Abstract: Deep Neural Networks are quite vulnerable to adversarial perturbations. Current state-of-the-art adversarial attack methods typically require very time consuming hyper-parameter tuning, or require many iterations to solve an optimization based adversarial attack. To address this problem, we present a new family of trust region based adversarial attacks, with the goal of computing adversarial perturbations efficiently. We propose several attacks based on variants of the trust region optimization method. We test the proposed methods on Cifar-10 and ImageNet datasets using several different models including AlexNet, ResNet-50, VGG-16, and DenseNet-121 models. Our methods achieve comparable results with the Carlini-Wagner (CW) attack, but with significant speed up of up to $37\times$, for the VGG-16 model on a Titan Xp GPU. For the case of ResNet-50 on ImageNet, we can bring down its classification accuracy to less than 0.1\% with at most $1.5\%$ relative $L_\infty$ (or $L_2$) perturbation requiring only $1.02$ seconds as compared to $27.04$ seconds for the CW attack. We have open sourced our method which can be accessed at [1].

Connected through hyperedge HE-1 (induced by source paper "a fast saddle point dynamical system approach to robust deep learning"):
  with members:
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust optimization for non convex objectives"
    - "practical black box attacks against machine learning"
    - "measuring neural net robustness with constraints"

Connected through hyperedge HE-2 (induced by source paper "jumprelu a retrofit defense strategy for adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deflecting adversarial attacks with pixel deflection"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "hawq v2 hessian aware trace weighted quantization of neural networks"):
  with members:
    - "q bert hessian based ultra low precision quantization of bert"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"

Connected through hyperedge HE-4 (induced by source paper "camus a framework to build formal specifications for deep perception systems using simulators"):
  with members:
    - "robust neural networks using randomized adversarial training"
    - "adversarial examples are a natural consequence of test error in noise"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a style based generator architecture for generative adversarial networks"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "carla an open urban driving simulator"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "large batch size training of neural networks with adversarial training and second order information"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "oversketched newton fast convex optimization for serverless systems"
    - "pyhessian neural networks through the lens of the hessian"
    - "inefficiency of k fac for large batch size training"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "image classification at supercomputer scale"

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)