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 releasing via deep generative model technical report"
  Abstract: Privacy-preserving releasing of complex data (e.g., image, text, audio) represents a long-standing challenge for the data mining research community. Due to rich semantics of the data and lack of a priori knowledge about the analysis task, excessive sanitization is often necessary to ensure privacy, leading to significant loss of the data utility. In this paper, we present dp-GAN, a general private releasing framework for semantic-rich data. Instead of sanitizing and then releasing the data, the data curator publishes a deep generative model which is trained using the original data in a differentially private manner; with the generative model, the analyst is able to produce an unlimited amount of synthetic data for arbitrary analysis tasks. In contrast of alternative solutions, dp-GAN highlights a set of key features: (i) it provides theoretical privacy guarantee via enforcing the differential privacy principle; (ii) it retains desirable utility in the released model, enabling a variety of otherwise impossible analyses; and (iii) most importantly, it achieves practical training scalability and stability by employing multi-fold optimization strategies. Through extensive empirical evaluation on benchmark datasets and analyses, we validate the efficacy of dp-GAN.

Connected through hyperedge HE-1 (induced by source paper "adaclip adaptive clipping for private sgd"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "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-2 (induced by source paper "differentially private survival function estimation"):
  with members:
    - "generating differentially private datasets using gans"
    - "differentially private generative adversarial network"
    - "differentially private regression for discrete time survival analysis"
    - "differentially private ordinary least squares"
    - "calibrating data to sensitivity in private data analysis"
    - "functional mechanism regression analysis under differential privacy"
    - "differentially private online learning"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "towards fair and decentralized privacy preserving deep learning with blockchain"):
  with members:
    - "modelchain decentralized privacy preserving healthcare predictive modeling framework on private blockchain networks"
    - "dimensionality driven learning with noisy labels"
    - "iterative learning with open set noisy labels"
    - "differentially private fair learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "crypto nets neural networks over encrypted data"

Connected through hyperedge HE-4 (induced by source paper "generating differentially private datasets using gans"):
  with members:
    - "differentially private generative adversarial network"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "self normalizing neural networks"
    - "plausible deniability for privacy preserving data synthesis"
    - "context aware generative adversarial privacy"
    - "improved training of wasserstein gans"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "learning privately from multiparty data"

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)