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: "testing identity of structured distributions"
  Abstract: We study the question of identity testing for structured distributions. More precisely, given samples from a {\em structured} distribution $q$ over $[n]$ and an explicit distribution $p$ over $[n]$, we wish to distinguish whether $q=p$ versus $q$ is at least $\epsilon$-far from $p$, in $L_1$ distance. In this work, we present a unified approach that yields new, simple testers, with sample complexity that is information-theoretically optimal, for broad classes of structured distributions, including $t$-flat distributions, $t$-modal distributions, log-concave distributions, monotone hazard rate (MHR) distributions, and mixtures thereof.

Connected through hyperedge HE-1 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples"):
  with members:
    - "towards testing monotonicity of distributions over general posets"
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "on learning mixtures of well separated gaussians"
    - "efficient robust proper learning of log concave distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of 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 "communication and memory efficient testing of discrete distributions"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "two party distribution testing communication and security"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "sharp bounds for generalized uniformity testing"
    - "generalized uniformity testing"
    - "testing conditional independence of discrete distributions"

Connected through hyperedge HE-4 (induced by source paper "testing properties of multiple distributions with few samples"):
  with members:
    - "generalized uniformity testing"
    - "learning populations of parameters"
    - "a new approach for testing properties of discrete distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing closeness of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "testing identity of multidimensional histograms"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "sharp bounds for generalized uniformity testing"
    - "fourier based testing for families of distributions"
    - "generalized uniformity testing"
    - "testing conditional independence of discrete distributions"
    - "a new approach for testing properties of discrete distributions"

Connected through hyperedge HE-6 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing"):
  with members:
    - "generalized uniformity testing"
    - "which distribution distances are sublinearly testable"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "faster sublinear algorithms using conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-7 (induced by source paper "distributed simulation and distributed inference"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "test without trust optimal locally private distribution testing"
    - "sample optimal identity testing with high probability"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "testing shape restrictions of discrete distributions"

Connected through hyperedge HE-8 (induced by source paper "near optimal closeness testing of discrete histogram distributions"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "a size free clt for poisson multinomials and its applications"
    - "fast algorithms for segmented regression"
    - "collision based testers are optimal for uniformity and closeness"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"

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)