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 "preserving statistical validity in adaptive data analysis"):
  Abstract: A great deal of effort has been devoted to reducing the risk of spurious scientific discoveries, from the use of sophisticated validation techniques, to deep statistical methods for controlling the false discovery rate in multiple hypothesis testing. However, there is a fundamental disconnect between the theoretical results and the practice of data analysis: the theory of statistical inference assumes a fixed collection of hypotheses to be tested, or learning algorithms to be applied, selected non-adaptively before the data are gathered, whereas in practice data is shared and reused with hypotheses and new analyses being generated on the basis of data exploration and the outcomes of previous analyses. #R##N#In this work we initiate a principled study of how to guarantee the validity of statistical inference in adaptive data analysis. As an instance of this problem, we propose and investigate the question of estimating the expectations of $m$ adaptively chosen functions on an unknown distribution given $n$ random samples. #R##N#We show that, surprisingly, there is a way to estimate an exponential in $n$ number of expectations accurately even if the functions are chosen adaptively. This gives an exponential improvement over standard empirical estimators that are limited to a linear number of estimates. Our result follows from a general technique that counter-intuitively involves actively perturbing and coordinating the estimates, using techniques developed for privacy preservation. We give additional applications of this technique to our question.

Includes members:
  Member "more general queries and less generalization error in adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic stability for adaptive data analysis")
  Member "the ladder a reliable leaderboard for machine learning competitions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do imagenet classifiers generalize to imagenet")
  Member "generalization in adaptive data analysis and holdout reuse":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "algorithmic stability for adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "preventing false discovery in interactive data analysis is hard":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "interactive fingerprinting codes and the hardness of preventing false discovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "interactive privacy via the median mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")

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)