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: "finite sample differentially private confidence intervals"
  Abstract: We study the problem of estimating finite sample confidence intervals of the mean of a normal population under the constraint of differential privacy. We consider both the known and unknown variance cases and construct differentially private algorithms to estimate confidence intervals. Crucially, our algorithms guarantee a finite sample coverage, as opposed to an asymptotic coverage. Unlike most previous differentially private algorithms, we do not require the domain of the samples to be bounded. We also prove lower bounds on the expected size of any differentially private confidence set showing that our the parameters are optimal up to polylogarithmic factors.

Connected through hyperedge HE-1 (induced by source paper "privately learning markov random fields"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "private hypothesis selection"
    - "data dependent differentially private parameter learning for directed graphical models"
    - "graphical model based estimation and inference for differential privacy"
    - "privately learning high dimensional distributions"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "inspectre privately estimating the unseen"
    - "learning graphical models using multiplicative weights"

Connected through hyperedge HE-2 (induced by source paper "private mean estimation of heavy tailed distributions"):
  with members:
    - "private hypothesis selection"
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "inspectre privately estimating the unseen"
    - "being robust in high dimensions can be practical"
    - "differentially private testing of identity and closeness of discrete distributions"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians"):
  with members:
    - "private hypothesis selection"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "differentially private k means with constant multiplicative error"
    - "inspectre privately estimating the unseen"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "clustering algorithms for the centralized and local models"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"

Connected through hyperedge HE-6 (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-7 (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"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-8 (induced by source paper "locally private gaussian estimation"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "the role of interactivity in local differential privacy"
    - "learning without interaction requires separation"
    - "differentially private hierarchical group size estimation"
    - "privately learning high dimensional distributions"
    - "differentially private hierarchical count of counts histograms"
    - "test without trust optimal locally private distribution testing"
    - "tight lower bounds for locally differentially private selection"

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)