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: "towards imperceptible and robust adversarial example attacks against neural networks"
  Abstract: Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input. Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes. In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world. In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example. Experimental results demonstrate the efficacy of the proposed technique.

Connected through hyperedge HE-1 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial attacks on face detectors using neural net based constrained optimization"

Connected through hyperedge HE-2 (induced by source paper "adversarially trained model compression when robustness meets efficiency"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-3 (induced by source paper "model compression with adversarial robustness a unified optimization framework"):
  with members:
    - "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"
    - "adversarial robustness may be at odds with simplicity"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "defensive quantization when efficiency meets robustness"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "sparse dnns with improved adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-4 (induced by source paper "towards large yet imperceptible adversarial image perturbations with perceptual color distance"):
  with members:
    - "perceptual quality preserving black box attack against deep learning image classifiers"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "smooth adversarial examples"
    - "a general framework for adversarial examples with objectives"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "there is limited correlation between coverage and robustness for deep neural networks"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "adversarial examples opportunities and challenges"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "testing deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "the attack generator a systematic approach towards constructing adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "playing the game of universal adversarial perturbations"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "capsattacks robust and imperceptible adversarial attacks on capsule networks"):
  with members:
    - "capsnet comparative performance evaluation for image classification"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "are adversarial examples inevitable"
    - "dolphinattack inaudible voice commands"
    - "enhancing robustness of machine learning systems via data transformations"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-8 (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"
    - "adversarial examples in deep learning characterization and divergence"
    - "sok security and privacy in machine learning"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "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"

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)