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: "differentially private model publishing for deep learning"
  Abstract: Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks. Large-scale training datasets are one of the critical factors for their success. However, when the training datasets are crowdsourced from individuals and contain sensitive information, the model parameters may encode private information and bear the risks of privacy leakage. The recent growing trend of the sharing and publishing of pre-trained models further aggravates such privacy risks. To tackle this problem, we propose a differentially private approach for training neural networks. Our approach includes several new techniques for optimizing both privacy loss and model accuracy. We employ a generalization of differential privacy called concentrated differential privacy(CDP), with both a formal and refined privacy loss analysis on two different data batching methods. We implement a dynamic privacy budget allocator over the course of training to improve model accuracy. Extensive experiments demonstrate that our approach effectively improves privacy loss accounting, training efficiency and model quality under a given privacy budget.

Connected through hyperedge HE-1 (induced by source paper "membership inference attacks against object detection models"):
  with members:
    - "a general approach to adding differential privacy to iterative training procedures"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "transferable adversarial attacks for image and video object detection"
    - "logan membership inference attacks against generative models"
    - "xgboost a scalable tree boosting system"
    - "ssd single shot multibox detector"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "differentially private empirical risk minimization revisited faster and more general"

Connected through hyperedge HE-3 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"
    - "detecting violations of differential privacy"

Connected through hyperedge HE-4 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"
    - "differentially private mixture of generative neural networks"

Connected through hyperedge HE-5 (induced by source paper "effects of differential privacy and data skewness on membership inference vulnerability"):
  with members:
    - "demystifying learning rate polices for high accuracy training of deep neural networks"
    - "cross layer strategic ensemble defense against adversarial examples"
    - "evaluating differentially private machine learning in practice"
    - "robust or private adversarial training makes models more vulnerable to privacy attacks"
    - "deep neural network ensembles against deception ensemble diversity accuracy and robustness"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "privacy preserving collaborative deep learning with unreliable participants"

Connected through hyperedge HE-6 (induced by source paper "adaclip adaptive clipping for private sgd"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "differentially private releasing via deep generative model technical report"
    - "federated learning for mobile keyboard prediction"
    - "optimal noise adding mechanism in additive differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "heterogeneous gaussian mechanism preserving differential privacy in deep learning with provable robustness"):
  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"
    - "sok security and privacy in machine learning"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving tensor factorization for collaborative health data analysis"):
  with members:
    - "federated tensor factorization for computational phenotyping"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "concentrated differential privacy"
    - "membership inference attacks against machine learning models"
    - "fast differentially private matrix factorization"
    - "deep learning with elastic averaging sgd"

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)