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: "adversarial examples that fool both computer vision and time limited humans"
  Abstract: Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.

Connected through hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "curriculum adversarial training"

Connected through hyperedge HE-2 (induced by source paper "recurrent attention model with log polar mapping is robust against adversarial attacks"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "spatially transformed adversarial examples"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "robustness of rotation equivariant networks to adversarial perturbations"
    - "polar transformer networks"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "optimizing privacy preserving outsourced convolutional neural network predictions"):
  with members:
    - "xonn xnor based oblivious deep neural network inference"
    - "chiron privacy preserving machine learning as a service"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "gazelle a low latency framework for secure neural network inference"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "logan membership inference attacks against generative models"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-4 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"

Connected through hyperedge HE-5 (induced by source paper "adversarial perturbations on the perceptual ball"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "interpretable counterfactual explanations guided by prototypes"
    - "universal adversarial perturbations against person re identification"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "counterfactual visual explanations"
    - "hardening deep neural networks via adversarial model cascades"

Connected through hyperedge HE-6 (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"
    - "generative adversarial examples"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "humans can decipher adversarial images"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-8 (induced by source paper "physgan generating physical world resilient adversarial examples for autonomous driving"):
  with members:
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "spatially transformed adversarial examples"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "lavan localized and visible adversarial noise"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "robust physical world attacks on deep learning models"

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)