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: "bolt on differential privacy for scalable stochastic gradient descent based analytics"
  Abstract: While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address \emph{both} issues in an integrated manner. In contrast to the white-box approach adopted by previous work, we revisit and use the classical technique of {\em output perturbation} to devise a novel "bolt-on" approach to private SGD. While our approach trivially addresses (2), it makes (1) even more challenging. We address this challenge by providing a novel analysis of the $L_2$-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We integrate our algorithm, as well as other state-of-the-art differentially private SGD, into Bismarck, a popular scalable SGD-based analytics system on top of an RDBMS. Extensive experiments show that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms on many real datasets.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"

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 "federating recommendations using differentially private prototypes"):
  with members:
    - "advances and open problems in federated learning"
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "practical differentially private top k selection with pay what you get composition"
    - "applied federated learning improving google keyboard query suggestions"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "federated learning for mobile keyboard prediction"
    - "federated tensor factorization for computational phenotyping"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-4 (induced by source paper "differentially private erm based on data perturbation"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "differentially private bayesian linear regression"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "efficient private erm for smooth objectives"
    - "collect at once use effectively making non interactive locally private learning possible"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

Connected through hyperedge HE-6 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "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"

Connected through hyperedge HE-7 (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-8 (induced by source paper "does learning require memorization a short tale about a long tail"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "towards demystifying membership inference attacks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"

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)