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: "private identity testing for high dimensional distributions"
  Abstract: In this work we present novel differentially private identity (goodness-of-fit) testers for natural and widely studied classes of multivariate product distributions: Gaussians in $\mathbb{R}^d$ with known covariance and product distributions over $\{\pm 1\}^{d}$. Our testers have improved sample complexity compared to those derived from previous techniques, and are the first testers whose sample complexity matches the order-optimal minimax sample complexity of $O(d^{1/2}/\alpha^2)$ in many parameter regimes. We construct two types of testers, exhibiting tradeoffs between sample complexity and computational complexity. Finally, we provide a two-way reduction between testing a subclass of multivariate product distributions and testing univariate distributions, and thereby obtain upper and lower bounds for testing this subclass of product distributions.

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"
    - "the optimal approximation factor in density estimation"

Connected through hyperedge HE-2 (induced by source paper "domain compression and its application to randomness optimal distributed goodness of fit"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "communication and memory efficient testing of discrete distributions"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving parametric inference a case for robust statistics"):
  with members:
    - "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"
    - "differentially private hypothesis testing revisited"
    - "convergence rates for differentially private statistical estimation"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "random restrictions of high dimensional distributions and uniformity testing with subcube conditioning"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "learning and testing causal models with interventions"

Connected through hyperedge HE-5 (induced by source paper "testing ising models"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "regression from dependent observations"
    - "learning from weakly dependent data under dobrushin s condition"
    - "learning and testing causal models with interventions"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "testing identity of multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"
    - "learning graphical models using multiplicative weights"

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)