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: "generalization for adaptively chosen estimators via stable median"
  Abstract: Datasets are often reused to perform multiple statistical analyses in an adaptive way, in which each analysis may depend on the outcomes of previous analyses on the same dataset. Standard statistical guarantees do not account for these dependencies and little is known about how to provably avoid overfitting and false discovery in the adaptive setting. We consider a natural formalization of this problem in which the goal is to design an algorithm that, given a limited number of i.i.d.~samples from an unknown distribution, can answer adaptively-chosen queries about that distribution. #R##N#We present an algorithm that estimates the expectations of $k$ arbitrary adaptively-chosen real-valued estimators using a number of samples that scales as $\sqrt{k}$. The answers given by our algorithm are essentially as accurate as if fresh samples were used to evaluate each estimator. In contrast, prior work yields error guarantees that scale with the worst-case sensitivity of each estimator. We also give a version of our algorithm that can be used to verify answers to such queries where the sample complexity depends logarithmically on the number of queries $k$ (as in the reusable holdout technique). #R##N#Our algorithm is based on a simple approximate median algorithm that satisfies the strong stability guarantees of differential privacy. Our techniques provide a new approach for analyzing the generalization guarantees of differentially private algorithms.

Connected through hyperedge HE-1 (induced by source paper "closure properties for private classification and online prediction"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "privately learning thresholds closing the exponential gap"
    - "the role of interactivity in local differential privacy"
    - "private learning implies online learning an efficient reduction"
    - "private pac learning implies finite littlestone dimension"
    - "online learning via differential privacy"
    - "differentially private release and learning of threshold functions"
    - "algorithmic stability for adaptive data analysis"

Connected through hyperedge HE-2 (induced by source paper "the power of synergy in differential privacy combining a small curator with local randomizers"):
  with members:
    - "locally private k means clustering"
    - "blender enabling local search with a hybrid differential privacy model"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "exponential separations in local differential privacy through communication complexity"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "the role of interactivity in local differential privacy"
    - "amplification by shuffling from local to central differential privacy via anonymity"

Connected through hyperedge HE-3 (induced by source paper "a new analysis of differential privacy s generalization guarantees"):
  with members:
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "guaranteed validity for empirical approaches to adaptive data analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "generalization bounds for uniformly stable algorithms"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "subgaussian tail bounds via stability arguments"

Connected through hyperedge HE-4 (induced by source paper "guaranteed validity for empirical approaches to adaptive data analysis"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "natural analysts in adaptive data analysis"
    - "model similarity mitigates test set overuse"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "max information differential privacy and post selection hypothesis testing"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-5 (induced by source paper "capacity bounded differential privacy"):
  with members:
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "a convex duality framework for gans"
    - "the inductive bias of restricted f gans"
    - "information privacy and stability in adaptive data analysis"
    - "approximation and convergence properties of generative adversarial learning"
    - "renyi differential privacy"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-6 (induced by source paper "the structure of optimal private tests for simple hypotheses"):
  with members:
    - "improved differentially private analysis of variance"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "inspectre privately estimating the unseen"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "differentially private identity and closeness testing of discrete distributions"
    - "calibrating noise to variance in adaptive data analysis"

Connected through hyperedge HE-7 (induced by source paper "the everlasting database statistical validity at a fair price"):
  with members:
    - "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"
    - "on the generalization properties of differential privacy"
    - "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-8 (induced by source paper "privacy preserving prediction"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "model agnostic private learning via stability"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "learning privately from multiparty data"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "characterizing the sample complexity of private learners"
    - "differentially private empirical risk minimization"

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)