human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sampling without compromising accuracy in adaptive data analysis"):
  Abstract: In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms. In particular, we describe a mechanism that provides a polynomial speed-up per query over previous mechanisms, without needing to increase the total amount of data required to maintain the same generalization error as before. We prove that this speed-up holds for arbitrary statistical queries. We also provide an even faster method for achieving statistically-meaningful responses wherein the mechanism is only allowed to see a constant number of samples from the data per query. Finally, we show that our general results yield a simple, fast, and unified approach for adaptively optimizing convex and strongly convex functions over a dataset.

Includes members:
  Member "privacy amplification by subsampling tight analyses via couplings and divergences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "max information differential privacy and post selection hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "differentially private release and learning of threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privately learning markov random fields")
  Member "statistical query algorithms for mean vector estimation and stochastic convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits on gradient compression for stochastic optimization")
  Member "generalization in adaptive data analysis and holdout reuse":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "between pure and approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privately learning markov random fields")
  Member "algorithmic stability for adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "preserving statistical validity in adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reasoning about generalization via conditional mutual information")

Question: Please tell me which class the source paper 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)