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: "gaussian differential privacy"
  Abstract: Differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy in the past decade. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analyzing important primitives like privacy amplification by subsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation, which we term `$f$-differential privacy' ($f$-DP). This notion of privacy has a number of appealing properties and, in particular, avoids difficulties associated with divergence based relaxations. First, $f$-DP preserves the hypothesis testing interpretation. In addition, $f$-DP allows for lossless reasoning about composition in an algebraic fashion. Moreover, we provide a powerful technique to import existing results proven for original DP to $f$-DP and, as an application, obtain a simple subsampling theorem for $f$-DP. #R##N#In addition to the above findings, we introduce a canonical single-parameter family of privacy notions within the $f$-DP class that is referred to as `Gaussian differential privacy' (GDP), defined based on testing two shifted Gaussians. GDP is focal among the $f$-DP class because of a central limit theorem we prove. More precisely, the privacy guarantees of \emph{any} hypothesis testing based definition of privacy (including original DP) converges to GDP in the limit under composition. The CLT also yields a computationally inexpensive tool for analyzing the exact composition of private algorithms. #R##N#Taken together, this collection of attractive properties render $f$-DP a mathematically coherent, analytically tractable, and versatile framework for private data analysis. Finally, we demonstrate the use of the tools we develop by giving an improved privacy analysis of noisy stochastic gradient descent.

Connected through hyperedge HE-1 (induced by source paper "deep learning with gaussian differential privacy"):
  with members:
    - "hypothesis testing interpretations and renyi differential privacy"
    - "adaclip adaptive clipping for private sgd"
    - "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "efficient private erm for smooth objectives"
    - "collecting telemetry data privately"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-2 (induced by source paper "hypothesis testing interpretations and renyi differential privacy"):
  with members:
    - "issues encountered deploying differential privacy"
    - "detecting violations of differential privacy"
    - "towards practical differential privacy for sql queries"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "approximate relational hoare logic for continuous random samplings"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differential privacy an economic method for choosing epsilon"
    - "the composition theorem for differential privacy"

Connected through hyperedge HE-3 (induced by source paper "element level differential privacy the right granularity of privacy"):
  with members:
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "learning differentially private recurrent language models"
    - "renyi differential privacy"
    - "concentrated differential privacy"
    - "deep residual learning for image recognition"
    - "between pure and approximate differential privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"):
  with members:
    - "practical differentially private top k selection with pay what you get composition"
    - "the role of interactivity in local differential privacy"
    - "privacy odometers and filters pay as you go composition"
    - "the complexity of computing the optimal composition of differential privacy"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"

Connected through hyperedge HE-5 (induced by source paper "ppgan privacy preserving generative adversarial network"):
  with members:
    - "reliable federated learning for mobile networks"
    - "differentially private generative adversarial network"
    - "a style based generator architecture for generative adversarial networks"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "improved training of wasserstein gans"
    - "deep models under the gan information leakage from collaborative deep learning"

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)