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: "darccc detecting adversaries by reconstruction from class conditional capsules"
  Abstract: We present a simple technique that allows capsule models to detect adversarial images. In addition to being trained to classify images, the capsule model is trained to reconstruct the images from the pose parameters and identity of the correct top-level capsule. Adversarial images do not look like a typical member of the predicted class and they have much larger reconstruction errors when the reconstruction is produced from the top-level capsule for that class. We show that setting a threshold on the $l2$ distance between the input image and its reconstruction from the winning capsule is very effective at detecting adversarial images for three different datasets. The same technique works quite well for CNNs that have been trained to reconstruct the image from all or part of the last hidden layer before the softmax. We then explore a stronger, white-box attack that takes the reconstruction error into account. This attack is able to fool our detection technique but in order to make the model change its prediction to another class, the attack must typically make the "adversarial" image resemble images of the other class.

Connected through hyperedge HE-1 (induced by source paper "understanding the limitations of conditional generative models"):
  with members:
    - "hybrid models with deep and invertible features"
    - "semi supervised learning with normalizing flows"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "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-2 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  with members:
    - "discretization based solutions for secure machine learning against adversarial attacks"
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "weight map layer for noise and adversarial attack robustness"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "simple black box adversarial attacks"

Connected through hyperedge HE-3 (induced by source paper "metaadvdet towards robust detection of evolving adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "spatially transformed adversarial examples"
    - "detecting adversarial examples via neural fingerprinting"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "rethinking feature distribution for loss functions in image classification"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-4 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "conditional generative models are not robust"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "humans can decipher adversarial images"
    - "deep predictive coding network for object recognition"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-5 (induced by source paper "mnist c a robustness benchmark for computer vision"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "a direct approach to robust deep learning using adversarial networks"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "on evaluating adversarial robustness"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-6 (induced by source paper "conditional generative models are not robust"):
  with members:
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "invertible residual networks"
    - "resisting adversarial attacks using gaussian mixture variational autoencoders"
    - "excessive invariance causes adversarial vulnerability"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-7 (induced by source paper "capsattacks robust and imperceptible adversarial attacks on capsule networks"):
  with members:
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "capsnet comparative performance evaluation for image classification"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "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 "on the vulnerability of capsule networks to adversarial attacks"):
  with members:
    - "capsattacks robust and imperceptible adversarial attacks on capsule networks"
    - "dense and diverse capsule networks making the capsules learn better"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deepfool a simple and accurate method to fool deep neural networks"

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)