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: "are facial attributes adversarially robust"
  Abstract: Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by training a deep convolutional neural network (DCNN) for each facial attribute separately, without using any pre-training or dataset augmentation, and we obtain new state-of-the-art facial attribute classification results on the CelebA benchmark. To test the stability of the networks, we generated adversarial images -- formed by adding imperceptible non-random perturbations to original inputs which result in classification errors -- via a novel fast flipping attribute (FFA) technique. We show that FFA generates more adversarial examples than other related algorithms, and that DCNNs for certain attributes are generally robust to adversarial inputs, while DCNNs for other attributes are not. This result is surprising because no DCNNs tested to date have exhibited robustness to adversarial images without explicit augmentation in the training procedure to account for adversarial examples. Finally, we introduce the concept of natural adversarial samples, i.e., images that are misclassified but can be easily turned into correctly classified images by applying small perturbations. We demonstrate that natural adversarial samples commonly occur, even within the training set, and show that many of these images remain misclassified even with additional training epochs. This phenomenon is surprising because correcting the misclassification, particularly when guided by training data, should require only a small adjustment to the DCNN parameters.

Connected through hyperedge HE-1 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "rank consistent ordinal regression for neural networks"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "right for the right reason training agnostic networks"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"

Connected through hyperedge HE-2 (induced by source paper "unsupervised enhancement of soft biometric privacy with negative face recognition"):
  with members:
    - "flowsan privacy enhancing semi adversarial networks to confound arbitrary face based gender classifiers"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "facial attributes accuracy and adversarial robustness"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "automatic malware description via attribute tagging and similarity embedding"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "fast zero shot image tagging"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "exploring bias in gan based data augmentation for small samples"):
  with members:
    - "gansfer learning combining labelled and unlabelled data for gan based data augmentation"
    - "imagining an engineer on gan based data augmentation perpetuating biases"
    - "t cgan conditional generative adversarial network for data augmentation in noisy time series with irregular sampling"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "pixel level data augmentation for semantic image segmentation using generative adversarial networks"
    - "prostategan mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"

Connected through hyperedge HE-5 (induced by source paper "sparsefool a few pixels make a big difference"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "spatially transformed adversarial examples"
    - "attacking convolutional neural network using differential evolution"
    - "audio adversarial examples targeted attacks on speech to text"
    - "with friends like these who needs adversaries"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "geometric robustness of deep networks analysis and improvement"

Connected through hyperedge HE-6 (induced by source paper "gradient band based adversarial training for generalized attack immunity of a3c path finding"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards proving the adversarial robustness of deep neural networks"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "extending defensive distillation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "defense against universal adversarial perturbations"

Connected through hyperedge HE-7 (induced by source paper "meade towards a malicious email attachment detection engine"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "open set intrusion recognition for fine grained attack categorization"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "xgboost a scalable tree boosting system"
    - "adversarial diversity and hard positive generation"

Connected through hyperedge HE-8 (induced by source paper "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "self normalizing neural networks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "partial transfer learning with selective adversarial networks"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "affact alignment free facial attribute classification technique"
    - "facenet a unified embedding for face recognition and clustering"

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)