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: "the composition theorem for differential privacy"
  Abstract: Sequential querying of differentially private mechanisms degrades the overall privacy level. In this paper, we answer the fundamental question of characterizing the level of overall privacy degradation as a function of the number of queries and the privacy levels maintained by each privatization mechanism. Our solution is complete: we prove an upper bound on the overall privacy level and construct a sequence of privatization mechanisms that achieves this bound. The key innovation is the introduction of an operational interpretation of differential privacy (involving hypothesis testing) and the use of new data processing inequalities. Our result improves over the state-of-the-art, and has immediate applications in several problems studied in the literature including differentially private multi-party computation.

Connected through hyperedge HE-1 (induced by source paper "dp cgan differentially private synthetic data and label generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "face aging with conditional generative adversarial networks"
    - "generating and designing dna with deep generative models"
    - "logan membership inference attacks against generative models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences"):
  with members:
    - "hypothesis testing interpretations and renyi differential privacy"
    - "privacy amplification by mixing and diffusion mechanisms"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "privacy amplification by iteration"
    - "renyi differential privacy"
    - "f divergence inequalities"

Connected through hyperedge HE-3 (induced by source paper "a blockchain based approach for saving and tracking differential privacy cost"):
  with members:
    - "zebralancer private and anonymous crowdsourcing system atop open blockchain"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "collecting telemetry data privately"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "generalization in adaptive data analysis and holdout reuse"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale"):
  with members:
    - "practical differentially private top k selection with pay what you get composition"
    - "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"
    - "pripearl a framework for privacy preserving analytics and reporting at linkedin"
    - "towards practical differential privacy for sql queries"
    - "collecting telemetry data privately"
    - "psi ψ a private data sharing interface"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "generative models for effective ml on private decentralized datasets"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "taxonomy of real faults in deep learning systems"
    - "reconstruction and membership inference attacks against generative models"
    - "towards federated learning at scale system design"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "evaluating differentially private machine learning in practice"
    - "videoflow a flow based generative model for video"
    - "detecting bias with generative counterfactual face attribute augmentation"

Connected through hyperedge HE-6 (induced by source paper "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"):
  with members:
    - "efficient privacy preserving nonconvex optimization"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "laplacian smoothing gradient descent"
    - "differentially private federated learning a client level perspective"
    - "differentially private learning of undirected graphical models using collective graphical models"

Connected through hyperedge HE-7 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"

Connected through hyperedge HE-8 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "linear queries estimation with local differential privacy"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "practical locally private heavy hitters"

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.DS(Data Structures and Algorithms)