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 "differentially private hypothesis testing revisited"):
  Abstract: Hypothesis testing is different from traditional applications of differential privacy in that one needs an accurate estimate of how the noise affects the result (i.e. a $p$-value). Previous approaches to differentially private hypothesis testing either used output perturbation techniques that generally had large sensitivities (hence risked swamping the data with noise), or input perturbation techniques that resulted in highly unreliable $p$-values (and hence invalid statistical conclusions). In this paper, we develop a variety of practical hypothesis tests that address these problems. Using a different asymptotic regime that is more suited to hypothesis testing with privacy, we show a modified equivalence between chi-squared tests and likelihood ratio tests. We then develop differentially private likelihood ratio and chi-squared tests for a variety of applications on tabular data (i.e., independence, homogeneity, and goodness-of-fit tests). An open problem is whether new test statistics specialized to differential privacy could lead to further improvements. To aid in this search, we further propose a permutation-based testbed that can allow experimenters to empirically estimate the behavior of new test statistics for private hypothesis testing before fully working out their mathematical details (such as approximate null distributions). Experimental evaluations on small and large datasets using a wide variety of privacy settings demonstrate the practicality and reliability of our methods.

Includes members:
  Member "revisiting differentially private hypothesis tests for categorical data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "convergence rates for differentially private statistical estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy preserving parametric inference a case for robust statistics")
  Member "composition attacks and auxiliary information in data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local differential privacy for deep learning")

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.CR(Cryptography and Security)