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: "optimal algorithms and lower bounds for testing closeness of structured distributions"
  Abstract: We give a general unified method that can be used for $L_1$ {\em closeness testing} of a wide range of univariate structured distribution families. More specifically, we design a sample optimal and computationally efficient algorithm for testing the equivalence of two unknown (potentially arbitrary) univariate distributions under the $\mathcal{A}_k$-distance metric: Given sample access to distributions with density functions $p, q: I \to \mathbb{R}$, we want to distinguish between the cases that $p=q$ and $\|p-q\|_{\mathcal{A}_k} \ge \epsilon$ with probability at least $2/3$. We show that for any $k \ge 2, \epsilon>0$, the {\em optimal} sample complexity of the $\mathcal{A}_k$-closeness testing problem is $\Theta(\max\{ k^{4/5}/\epsilon^{6/5}, k^{1/2}/\epsilon^2 \})$. This is the first $o(k)$ sample algorithm for this problem, and yields new, simple $L_1$ closeness testers, in most cases with optimal sample complexity, for broad classes of structured distributions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "lower bounds for testing graphical models colorings and antiferromagnetic ising models"):
  with members:
    - "approximating pairwise correlations in the ising model"
    - "learning graphical models using multiplicative weights"
    - "sample optimal identity testing with high probability"
    - "structure learning of h colorings"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-3 (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-4 (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"

Connected through hyperedge HE-5 (induced by source paper "sharp bounds for generalized uniformity testing"):
  with members:
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "fourier based testing for families of distributions"
    - "generalized uniformity testing"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "collision based testers are optimal for uniformity and closeness"

Connected through hyperedge HE-6 (induced by source paper "testing conditional independence of discrete distributions"):
  with members:
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "fourier based testing for families of distributions"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "equality of opportunity in supervised learning"
    - "collision based testers are optimal for uniformity and closeness"

Connected through hyperedge HE-7 (induced by source paper "optimal identity testing with high probability"):
  with members:
    - "near optimal closeness testing of discrete histogram distributions"
    - "fourier based testing for families of distributions"
    - "a new approach for testing properties of discrete distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"
    - "optimal testing for properties of distributions"
    - "testing shape restrictions of discrete distributions"
    - "testing identity of structured distributions"

Connected through hyperedge HE-8 (induced by source paper "differentially private identity and closeness testing of discrete distributions"):
  with members:
    - "near optimal closeness testing of discrete histogram distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"
    - "differentially private hypothesis testing revisited"
    - "optimal testing for properties of distributions"
    - "testing shape restrictions of discrete distributions"
    - "testing identity of structured distributions"
    - "optimal algorithms for testing closeness of discrete distributions"

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)