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: "the structure of optimal private tests for simple hypotheses"
  Abstract: Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privately testing simple hypotheses: given two distributions $P$ and $Q$, and a privacy level $\varepsilon$, how many i.i.d. samples are needed to distinguish $P$ from $Q$ subject to $\varepsilon$-differential privacy, and what sort of tests have optimal sample complexity? Specifically, we characterize this sample complexity up to constant factors in terms of the structure of $P$ and $Q$ and the privacy level $\varepsilon$, and show that this sample complexity is achieved by a certain randomized and clamped variant of the log-likelihood ratio test. Our result is an analogue of the classical Neyman-Pearson lemma in the setting of private hypothesis testing. We also give an application of our result to the private change-point detection. Our characterization applies more generally to hypothesis tests satisfying essentially any notion of algorithmic stability, which is known to imply strong generalization bounds in adaptive data analysis, and thus our results have applications even when privacy is not a primary concern.

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 hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

Connected through hyperedge HE-3 (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-4 (induced by source paper "the role of interactivity in local differential privacy"):
  with members:
    - "exponential separations in local differential privacy"
    - "the privacy blanket of the shuffle model"
    - "learning without interaction requires separation"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "towards instance optimal private query release"
    - "test without trust optimal locally private distribution testing"
    - "locally private gaussian estimation"
    - "tight lower bounds for locally differentially private selection"

Connected through hyperedge HE-5 (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"
    - "revisiting differentially private hypothesis tests for categorical data"
    - "differentially private ordinary least squares"
    - "differentially private hypothesis testing revisited"
    - "convergence rates for differentially private statistical estimation"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-6 (induced by source paper "kng the k norm gradient mechanism"):
  with members:
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "lower bounds for differential privacy from gaussian width"
    - "differentially private ordinary least squares"
    - "calibrating data to sensitivity in private data analysis"
    - "functional mechanism regression analysis under differential privacy"
    - "on the geometry of differential privacy"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "profile based privacy for locally private computations"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "distributed learning communication complexity and privacy"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "benefits and pitfalls of the exponential mechanism with applications to hilbert spaces and functional pca"):
  with members:
    - "calibrating data to sensitivity in private data analysis"

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.DS(Data Structures and Algorithms)