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 stochastic gradient descent for in rdbms analytics"
  Abstract: In-RDBMS data analysis has received considerable attention in the past decade and has been widely used in sensitive domains to extract patterns in data using machine learning. For these domains, there has also been growing concern about privacy, and differential privacy has emerged as the gold standard for private data analysis. However, while differentially private machine learning and in-RDBMS data analytics have been studied separately, little previous work has explored private learning in an in-RDBMS system. #R##N#This work considers a specific algorithm --- stochastic gradient descent (SGD) for differentially private machine learning --- and explores how to integrate it into an RDBMS system. We find that previous solutions on differentially private SGD have characteristics that render them unattractive for an RDBMS implementation. To address this, we propose an algorithm that runs SGD for a constant number of passes and then adds noise to the resulting output. We provide a novel analysis of the privacy properties of this algorithm. We then integrate it, along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS, incurs virtually no overhead, and yields substantially better test accuracy than the other private SGD algorithm implementations.

Connected through hyperedge HE-1 (induced by source paper "differentially private distributed data summarization under covariate shift"):
  with members:
    - "clustering algorithms for the centralized and local models"
    - "learning privately from multiparty data"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep domain confusion maximizing for domain invariance"
    - "the composition theorem for differential privacy"
    - "a simple and practical algorithm for differentially private data release"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "differentially private mixture of generative neural networks"):
  with members:
    - "plausible deniability for privacy preserving data synthesis"
    - "cnn based joint clustering and representation learning with feature drift compensation for large scale image data"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "semantic image inpainting with perceptual and contextual losses"
    - "joint unsupervised learning of deep representations and image clusters"

Connected through hyperedge HE-3 (induced by source paper "differentially private learning of undirected graphical models using collective graphical models"):
  with members:
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "on the differential privacy of bayesian inference"
    - "gaussian approximation of collective graphical models"
    - "calibrating data to sensitivity in private data analysis"
    - "a simple and practical algorithm for differentially private data release"
    - "differentially private empirical risk minimization"
    - "learning in a large function space privacy preserving mechanisms for svm learning"
    - "what can we learn privately"

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.LG(Machine Learning)