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: "facial attributes accuracy and adversarial robustness"
  Abstract: Abstract   Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract facial attributes using deep neural networks (DNNs) trained on labeled facial attribute data, the robustness of deep attribute representations has not been evaluated. In this paper, we examine the representational stability of several approaches that recently advanced the state of the art on the CelebA benchmark by generating adversarial examples formed by adding small, non-random perturbations to inputs yielding altered classifications. We show that our fast flipping attribute (FFA) technique generates more adversarial examples than traditional algorithms, and that the adversarial robustness of DNNs varies highly between facial attributes. We also test the correlation of facial attributes and find that only for related attributes do the formed adversarial perturbations change the classification of others. Finally, we introduce the concept of natural adversarial samples, i.e., misclassified images where predictions can be corrected via small perturbations. We demonstrate that natural adversarial samples commonly occur and show that many of these images remain misclassified even with additional training epochs, even though their correct classification may require only a small adjustment to network parameters.

Connected through hyperedge HE-1 (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"
    - "are facial attributes adversarially robust"
    - "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-2 (induced by source paper "open dnn box by power side channel attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "finefool fine object contour attack via attention"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "stealing neural networks via timing side channels"
    - "accelerator aware pruning for convolutional neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "nisp pruning networks using neuron importance score propagation"

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"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "fast zero shot image tagging"
    - "are facial attributes adversarially robust"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-4 (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-5 (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"
    - "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"
    - "are facial attributes adversarially robust"

Connected through hyperedge HE-6 (induced by source paper "anonymizing k facial attributes via adversarial perturbations"):
  with members:
    - "residual codean autoencoder for facial attribute analysis"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "are facial attributes adversarially robust"
    - "towards evaluating the robustness of neural networks"
    - "multi task cnn model for attribute prediction"
    - "deep learning face attributes in the wild"

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)