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: "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.

Connected through hyperedge HE-1 (induced by source paper "locally private hypothesis selection"):
  with members:
    - "private hypothesis selection"
    - "pan private uniformity testing"
    - "sorted top k in rounds"
    - "improved differentially private analysis of variance"
    - "locally private learning without interaction requires separation"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "private identity testing for high dimensional distributions"

Connected through hyperedge HE-2 (induced by source paper "private identity testing for high dimensional distributions"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "improved differentially private analysis of variance"
    - "individual sensitivity preprocessing for data privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "learning and testing causal models with interventions"
    - "privately learning high dimensional distributions"
    - "test without trust optimal locally private distribution testing"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving parametric inference a case for robust statistics"):
  with members:
    - "private identity testing for high dimensional distributions"
    - "locally private hypothesis testing"
    - "differentially private bayesian inference for exponential families"
    - "the structure of optimal private tests for simple hypotheses"
    - "revisiting differentially private hypothesis tests for categorical data"
    - "differentially private ordinary least squares"
    - "convergence rates for differentially private statistical estimation"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "improved differentially private analysis of variance"):
  with members:
    - "comparing population means under local differential privacy with significance and power"
    - "differentially private anova testing"
    - "differentially private regression for discrete time survival analysis"
    - "revisiting differentially private hypothesis tests for categorical data"
    - "differentially private ordinary least squares"
    - "differentially private ordinary least squares t values confidence intervals and rejecting null hypotheses"
    - "calibrating data to sensitivity in private data analysis"
    - "efficient differentially private point estimators"

Connected through hyperedge HE-5 (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"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "differentially private identity and closeness testing of discrete distributions"

Connected through hyperedge HE-6 (induced by source paper "privately learning high dimensional distributions"):
  with members:
    - "locally private hypothesis testing"
    - "inspectre privately estimating the unseen"
    - "the price of selection in differential privacy"
    - "being robust in high dimensions can be practical"
    - "differentially private identity and closeness testing of discrete distributions"
    - "tight lower bounds for differentially private selection"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "finite sample differentially private confidence intervals"

Connected through hyperedge HE-7 (induced by source paper "locally private mean estimation z test and tight confidence intervals"):
  with members:
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "practical locally private heavy hitters"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "finite sample differentially private confidence intervals"
    - "heavy hitters and the structure of local privacy"
    - "priv it private and sample efficient identity testing"
    - "dealing with range anxiety in mean estimation via statistical queries"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial privacy"):
  with members:
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "deep variational information bottleneck"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "censoring representations with an adversary"
    - "conditional generative adversarial nets"
    - "very deep convolutional networks for large scale image recognition"

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.CR(Cryptography and Security)