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: "detection based defense against adversarial examples from the steganalysis point of view"
  Abstract: Deep Neural Networks (DNNs) have recently led to significant improvements in many fields. However, DNNs are vulnerable to adversarial examples which are samples with imperceptible perturbations while dramatically misleading the DNNs. Moreover, adversarial examples can be used to perform an attack on various kinds of DNN based systems, even if the adversary has no access to the underlying model. Many defense methods have been proposed, such as obfuscating gradients of the networks or detecting adversarial examples. However it is proved out that these defense methods are not effective or cannot resist secondary adversarial attacks. In this paper, we point out that steganalysis can be applied to adversarial examples detection, and propose a method to enhance steganalysis features by estimating the probability of modifications caused by adversarial attacks. Experimental results show that the proposed method can accurately detect adversarial examples. Moreover, secondary adversarial attacks cannot be directly performed to our method because our method is not based on a neural network but based on high-dimensional artificial features and FLD (Fisher Linear Discriminant) ensemble.

Connected through hyperedge HE-1 (induced by source paper "visionguard runtime detection of adversarial inputs to perception systems"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "lavan localized and visible adversarial noise"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-2 (induced by source paper "toward adversarial robustness via semi supervised robust training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "theoretically principled trade off between robustness and accuracy"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"

Connected through hyperedge HE-3 (induced by source paper "code bridged classifier cbc a low or negative overhead defense for making a cnn classifier robust against adversarial attacks"):
  with members:
    - "tcd npe a re configurable and efficient neural processing engine powered by novel temporal carry deferring macs"
    - "nesta hamming weight compression based neural proc engine"
    - "magnet a two pronged defense against adversarial examples"
    - "towards robust detection of adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "practical black box attacks against machine learning"
    - "universal adversarial perturbations"

Connected through hyperedge HE-4 (induced by source paper "had gan a human perception auxiliary defense gan to defend adversarial examples"):
  with members:
    - "robust neural networks using randomized adversarial training"
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "generating adversarial examples with conditional generative adversarial net"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning to characterize adversarial subspaces"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "advhat real world adversarial attack on arcface face id system"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "attention is all you need"
    - "practical black box attacks against machine learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial embedding a robust and elusive steganography and watermarking technique"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the adversarial attack and detection under the fisher information metric"
    - "hidden hiding data with deep networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "steganographic generative adversarial networks"
    - "on the statistical detection of adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "adversarial defense via local flatness regularization"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "theoretically principled trade off between robustness and accuracy"
    - "interpreting and evaluating neural network robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "densepose dense human pose estimation in the wild"
    - "relation networks for object detection"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "dup net denoiser and upsampler network for 3d adversarial point clouds defense"):
  with members:
    - "adversarial attack and defense on point sets"
    - "one pixel attack for fooling deep neural networks"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "realistic adversarial examples in 3d meshes"
    - "generating 3d adversarial point clouds"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deep sets"

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)