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 testing of identity and closeness of discrete distributions"
  Abstract: We study the fundamental problems of identity testing (goodness of fit), and closeness testing (two sample test) of distributions over $k$ elements, under differential privacy. While the problems have a long history in statistics, finite sample bounds for these problems have only been established recently. #R##N#In this work, we derive upper and lower bounds on the sample complexity of both the problems under $(\varepsilon, \delta)$-differential privacy. We provide optimal sample complexity algorithms for identity testing problem for all parameter ranges, and the first results for closeness testing. Our closeness testing bounds are optimal in the sparse regime where the number of samples is at most $k$. #R##N#Our upper bounds are obtained by privatizing non-private estimators for these problems. The non-private estimators are chosen to have small sensitivity. We propose a general framework to establish lower bounds on the sample complexity of statistical tasks under differential privacy. We show a bound on differentially private algorithms in terms of a coupling between the two hypothesis classes we aim to test. By constructing carefully chosen priors over the hypothesis classes, and using Le Cam's two point theorem we provide a general mechanism for proving lower bounds. We believe that the framework can be used to obtain strong lower bounds for other statistical tasks under privacy.

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 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"
    - "finite sample differentially private confidence intervals"

Connected through hyperedge HE-3 (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-4 (induced by source paper "blender enabling local search with a hybrid differential privacy model"):
  with members:
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
    - "privacy amplification by iteration"
    - "the power of the hybrid model for mean estimation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "differentially private identity and closeness testing of discrete distributions"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-5 (induced by source paper "pan private uniformity testing"):
  with members:
    - "exponential separations in local differential privacy"
    - "communication and memory efficient testing of discrete distributions"
    - "the role of interactivity in local differential privacy"
    - "test without trust optimal locally private distribution testing"
    - "usable differential privacy a case study with psi"
    - "distributed differential privacy via shuffling"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"

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"
    - "finite sample differentially private confidence intervals"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-8 (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"
    - "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"
    - "differentially private ordinary least squares"

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.LG(Machine Learning)