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: "privacy preserving machine learning through data obfuscation"
  Abstract: As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. #R##N#In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce an obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.

Connected through hyperedge HE-1 (induced by source paper "predicting performance of asynchronous differentially private learning"):
  with members:
    - "the value of collaboration in convex machine learning with differential privacy"
    - "dp admm admm based distributed learning with differential privacy"
    - "faithful implementations of distributed algorithms and control laws"
    - "learning differentially private recurrent language models"
    - "asynchronous methods for deep reinforcement learning"
    - "on the differential privacy of bayesian inference"
    - "functional mechanism regression analysis under differential privacy"

Connected through hyperedge HE-2 (induced by source paper "artificial intelligence for digital agriculture at scale techniques policies and challenges"):
  with members:
    - "auto tuning neural network quantization framework for collaborative inference between the cloud and edge"
    - "edge cloud offloading algorithms issues methods and perspectives"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "low power wide area networks an overview"

Connected through hyperedge HE-3 (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"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "style aggregated network for facial landmark detection"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "segmentations leak membership inference attacks and defenses in semantic image segmentation"):
  with members:
    - "disentangling adversarial robustness and generalization"
    - "knockoff nets stealing functionality of black box models"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "unified perceptual parsing for scene understanding"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-5 (induced by source paper "key protected classification for collaborative learning"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "adversarial attacks and defences a survey"
    - "differentially private generative adversarial network"
    - "efficient deep learning on multi source private data"
    - "personalized and private peer to peer machine learning"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-6 (induced by source paper "an adaptive and fast convergent approach to differentially private deep learning"):
  with members:
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "boosting model performance through differentially private model aggregation"
    - "renyi differential privacy"
    - "geographic differential privacy for mobile crowd coverage maximization"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"

Connected through hyperedge HE-7 (induced by source paper "the value of collaboration in convex machine learning with differential privacy"):
  with members:
    - "chiron privacy preserving machine learning as a service"
    - "dp admm admm based distributed learning with differential privacy"
    - "learning differentially private recurrent language models"
    - "on the differential privacy of bayesian inference"
    - "differentially private distributed optimization"
    - "functional mechanism regression analysis under differential privacy"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-8 (induced by source paper "on analyzing and evaluating privacy measures for social networks under active attack"):
  with members:
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "motifs in temporal networks"
    - "on optimal approximability results for computing the strong metric dimension"
    - "on the computational complexities of three privacy measures for large networks under active attack"
    - "computing the diameter polynomially faster than apsp"

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)