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: "defeating image obfuscation with deep learning"
  Abstract: We demonstrate that modern image recognition methods based on artificial neural networks can recover hidden information from images protected by various forms of obfuscation. The obfuscation techniques considered in this paper are mosaicing (also known as pixelation), blurring (as used by YouTube), and P3, a recently proposed system for privacy-preserving photo sharing that encrypts the significant JPEG coefficients to make images unrecognizable by humans. We empirically show how to train artificial neural networks to successfully identify faces and recognize objects and handwritten digits even if the images are protected using any of the above obfuscation techniques.

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 "privacy preserving image sharing via sparsifying layers on convolutional groups"):
  with members:
    - "learning to compress and search visual data in large scale systems"
    - "generative adversarial networks for extreme learned image compression"
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "context aware generative adversarial privacy"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "linguistic fingerprints of internet censorship the case of sinaweibo"):
  with members:
    - "linguistic characteristics of censorable language on sinaweibo"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "the velocity of censorship high fidelity detection of microblog post deletions"

Connected through hyperedge HE-4 (induced by source paper "revealing scenes by inverting structure from motion reconstructions"):
  with members:
    - "privacy preserving image based localization"
    - "learning privacy preserving encodings through adversarial training"
    - "deep multi scale architectures for monocular depth estimation"
    - "semi parametric image synthesis"
    - "megadepth learning single view depth prediction from internet photos"
    - "image manipulation with perceptual discriminators"
    - "estimating depth from rgb and sparse sensing"
    - "smart sparse contours to represent and edit images"

Connected through hyperedge HE-5 (induced by source paper "on low resolution face recognition in the wild comparisons and new techniques"):
  with members:
    - "generative adversarial networks an overview"
    - "additive margin softmax for face verification"
    - "deep semantic face deblurring"
    - "face super resolution through wasserstein gans"
    - "deep face deblurring"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-6 (induced by source paper "deep poisoning functions towards robust privacy safe image data sharing"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "the deepfake detection challenge dfdc preview dataset"
    - "training with the invisibles obfuscating images to share safely for learning visual recognition models"
    - "deepobfuscator adversarial training framework for privacy preserving image classification"
    - "privacy preserving image based localization"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a hybrid model for identity obfuscation by face replacement"

Connected through hyperedge HE-7 (induced by source paper "anonymousnet natural face de identification with measurable privacy"):
  with members:
    - "beauty learning and counterfactual inference"
    - "understanding beauty via deep facial features"
    - "semi supervised text regression with conditional generative adversarial networks"
    - "privacy protective gan for face de identification"
    - "technical privacy metrics a systematic survey"
    - "privacy preserving machine learning through data obfuscation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-8 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "estimation efficiency under privacy constraints"
    - "distributed generation of privacy preserving data with user customization"
    - "on the robustness of information theoretic privacy measures and mechanisms"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "generative adversarial privacy"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"

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.CR(Cryptography and Security)