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: "on the generalization properties of differential privacy"
  Abstract: A new line of work, started with Dwork et al., studies the task of answering statistical queries using a sample and relates the problem to the concept of differential privacy. By the Hoeffding bound, a sample of size $O(\log k/\alpha^2)$ suffices to answer $k$ non-adaptive queries within error $\alpha$, where the answers are computed by evaluating the statistical queries on the sample. This argument fails when the queries are chosen adaptively (and can hence depend on the sample). Dwork et al. showed that if the answers are computed with $(\epsilon,\delta)$-differential privacy then $O(\epsilon)$ accuracy is guaranteed with probability $1-O(\delta^\epsilon)$. Using the Private Multiplicative Weights mechanism, they concluded that the sample size can still grow polylogarithmically with the $k$. #R##N#Very recently, Bassily et al. presented an improved bound and showed that (a variant of) the private multiplicative weights algorithm can answer $k$ adaptively chosen statistical queries using sample complexity that grows logarithmically in $k$. However, their results no longer hold for every differentially private algorithm, and require modifying the private multiplicative weights algorithm in order to obtain their high probability bounds. #R##N#We greatly simplify the results of Dwork et al. and improve on the bound by showing that differential privacy guarantees $O(\epsilon)$ accuracy with probability $1-O(\delta\log(1/\epsilon)/\epsilon)$. It would be tempting to guess that an $(\epsilon,\delta)$-differentially private computation should guarantee $O(\epsilon)$ accuracy with probability $1-O(\delta)$. However, we show that this is not the case, and that our bound is tight (up to logarithmic factors).

Connected through hyperedge HE-1 (induced by source paper "mixmatch a holistic approach to semi supervised learning"):
  with members:
    - "deep metric transfer for label propagation with limited annotated data"
    - "autoaugment learning augmentation policies from data"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "decoupled weight decay regularization"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "on the intrinsic privacy of stochastic gradient descent"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "evaluating differentially private machine learning in practice"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep reinforcement learning that matters"
    - "differentially private federated learning a client level perspective"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-3 (induced by source paper "generalization bounds for uniformly stable algorithms"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "privacy preserving prediction"
    - "data dependent stability of stochastic gradient descent"
    - "subgaussian tail bounds via stability arguments"
    - "concentration bounds for high sensitivity functions through differential privacy"
    - "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"

Connected through hyperedge HE-4 (induced by source paper "the everlasting database statistical validity at a fair price"):
  with members:
    - "generalization for adaptively chosen estimators via stable median"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-5 (induced by source paper "turning lemons into peaches using secure computation"):
  with members:
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (induced by source paper "online learning via the differential privacy lens"):
  with members:
    - "introduction to online convex optimization"
    - "private pac learning implies finite littlestone dimension"
    - "make the minority great again first order regret bound for contextual bandits"
    - "achieving privacy in the adversarial multi armed bandit"
    - "the price of differential privacy for online learning"
    - "adaptive learning with robust generalization guarantees"
    - "fighting bandits with a new kind of smoothness"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "online learning via differential privacy"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "pca with gaussian perturbations"
    - "fighting bandits with a new kind of smoothness"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "first order regret bounds for combinatorial semi bandits"
    - "online linear optimization via smoothing"

Connected through hyperedge HE-8 (induced by source paper "algorithmic stability for adaptive data analysis"):
  with members:
    - "more general queries and less generalization error in adaptive data analysis"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "between pure and approximate differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

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)