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: "deepobfuscator adversarial training framework for privacy preserving image classification"
  Abstract: Deep learning has been widely utilized in many computer vision applications and achieved remarkable commercial success. However, running deep learning models on mobile devices is generally challenging due to limitation of the available computing resources. It is common to let the users send their service requests to cloud servers that run the large-scale deep learning models to process. Sending the data associated with the service requests to the cloud, however, impose risks on the user data privacy. Some prior arts proposed sending the features extracted from raw data (e.g., images) to the cloud. Unfortunately, these extracted features can still be exploited by attackers to recover raw images and to infer embedded private attributes (e.g., age, gender, etc.). In this paper, we propose an adversarial training framework DeepObfuscator that can prevent extracted features from being utilized to reconstruct raw images and infer private attributes, while retaining the useful information for the intended cloud service (i.e., image classification). DeepObfuscator includes a learnable encoder, namely, obfuscator that is designed to hide privacy-related sensitive information from the features by performingour proposed adversarial training algorithm. Our experiments on CelebAdataset show that the quality of the reconstructed images fromthe obfuscated features of the raw image is dramatically decreased from 0.9458 to 0.3175 in terms of multi-scale structural similarity (MS-SSIM). The person in the reconstructed image, hence, becomes hardly to be re-identified. The classification accuracy of the inferred private attributes that can be achieved by the attacker drops down to a random-guessing level, e.g., the accuracy of gender is reduced from 97.36% to 58.85%. As a comparison, the accuracy of the intended classification tasks performed via the cloud service drops by only 2%

Connected through hyperedge HE-1 (induced by source paper "protecting gans against privacy attacks by preventing overfitting"):
  with members:
    - "gan leaks a taxonomy of membership inference attacks against gans"
    - "generative adversarial network in medical imaging a review"
    - "differentially private generative adversarial network"
    - "privacy preserving machine learning threats and solutions"
    - "logan membership inference attacks against generative models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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"
    - "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"
    - "vggface2 a dataset for recognising faces across pose and age"

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)