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: "p3 toward privacy preserving photo sharing"
  Abstract: With increasing use of mobile devices, photo sharing services are experiencing greater popularity. Aside from providing storage, photo sharing services enable bandwidth-efficient downloads to mobile devices by performing server-side image transformations (resizing, cropping). On the flip side, photo sharing services have raised privacy concerns such as leakage of photos to unauthorized viewers and the use of algorithmic recognition technologies by providers. To address these concerns, we propose a privacy-preserving photo encoding algorithm that extracts and encrypts a small, but significant, component of the photo, while preserving the remainder in a public, standards-compatible, part. These two components can be separately stored. This technique significantly reduces the signal-to-noise ratio and the accuracy of automated detection and recognition on the public part, while preserving the ability of the provider to perform server-side transformations to conserve download bandwidth usage. Our prototype privacy-preserving photo sharing system, P3, works with Facebook, and can be extended to other services as well. P3 requires no changes to existing services or mobile application software, and adds minimal photo storage overhead.

Connected through hyperedge HE-1 (induced by source paper "security and privacy approaches in mixed reality a literature survey"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "magnet a two pronged defense against adversarial examples"
    - "efficient defenses against adversarial attacks"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "cryptoimg privacy preserving processing over encrypted images"
    - "cardea context aware visual privacy protection from pervasive cameras"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "risk perceptions for wearable devices"

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"
    - "defeating image obfuscation with deep learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "robustness analysis of face obscuration"):
  with members:
    - "a utility preserving gan for face obscuration"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "in defense of the triplet loss for person re identification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "natural and effective obfuscation by head inpainting"

Connected through hyperedge HE-5 (induced by source paper "face recognition in low quality images a survey"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "coupled learning for facial deblur"
    - "super identity convolutional neural network for face hallucination"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "low resolution face recognition"
    - "deep semantic face deblurring"
    - "surveillance face recognition challenge"
    - "face super resolution through wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "distribution discrepancy maximization for image privacy preserving"):
  with members:
    - "multi scale face restoration with sequential gating ensemble network"
    - "conditional image to image translation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "perceptual losses for real time style transfer and super resolution"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "faceless person recognition privacy implications in social media"
    - "defeating image obfuscation with deep learning"

Connected through hyperedge HE-7 (induced by source paper "photosafer content based and context aware private photo protection for smartphones"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensorflow a system for large scale machine learning"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "encryption then compression systems using grayscale based image encryption for jpeg images"):
  with members:
    - "cryptanalyzing an image scrambling encryption algorithm of pixel bits"

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)