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: "can we still avoid automatic face detection"
  Abstract: After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? #R##N#In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing "privacy glasses" still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.

Connected through hyperedge HE-1 (induced by source paper "towards privacy protection by generating adversarial identity masks"):
  with members:
    - "live face de identification in video"
    - "analyzing and improving the image quality of stylegan"
    - "anonymousnet natural face de identification with measurable privacy"
    - "cosface large margin cosine loss for deep face recognition"
    - "privacy protective gan for face de identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "a hybrid model for identity obfuscation by face replacement"

Connected through hyperedge HE-2 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches"):
  with members:
    - "person recognition in personal photo collections"
    - "one pixel attack for fooling deep neural networks"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "sequential attacks on agents for long term adversarial goals"

Connected through hyperedge HE-3 (induced by source paper "face recognition in unconstrained conditions a systematic review"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "face detection through scale friendly deep convolutional networks"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

Connected through hyperedge HE-4 (induced by source paper "psyphy a psychophysics driven evaluation framework for visual recognition"):
  with members:
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "how deep is the feature analysis underlying rapid visual categorization"
    - "towards open set deep networks"
    - "a deeper look at dataset bias"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "speaking the same language matching machine to human captions by adversarial training"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "adversarial learning for neural dialogue generation"
    - "creativity generating diverse questions using variational autoencoders"
    - "face detection using deep learning an improved faster rcnn approach"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "image captioning with semantic attention"
    - "faceless person recognition privacy implications in social media"

Connected through hyperedge HE-6 (induced by source paper "deeptest automated testing of deep neural network driven autonomous cars"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "robust physical world attacks on deep learning models"
    - "certified defenses for data poisoning attacks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "extending defensive distillation"
    - "robust physical world attacks on machine learning models"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "simple black box adversarial perturbations for deep networks"

Connected through hyperedge HE-7 (induced by source paper "adversarial image perturbation for privacy protection a game theory perspective"):
  with members:
    - "person recognition in personal photo collections"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "a domain based approach to social relation recognition"
    - "simple black box adversarial perturbations for deep networks"
    - "assessing threat of adversarial examples on deep neural networks"
    - "faceless person recognition privacy implications in social media"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial diversity and hard positive generation"

Connected through hyperedge HE-8 (induced by source paper "towards practical verification of machine learning the case of computer vision systems"):
  with members:
    - "sok security and privacy in machine learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "practical black box attacks against machine learning"
    - "end to end learning for self driving cars"

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)