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 learning via the fourier transform for sums of independent integer random variables"
  Abstract: We study the structure and learnability of sums of independent integer random variables (SIIRVs). For $k \in \mathbb{Z}_{+}$, a $k$-SIIRV of order $n \in \mathbb{Z}_{+}$ is the probability distribution of the sum of $n$ independent random variables each supported on $\{0, 1, \dots, k-1\}$. We denote by ${\cal S}_{n,k}$ the set of all $k$-SIIRVs of order $n$. #R##N#In this paper, we tightly characterize the sample and computational complexity of learning $k$-SIIRVs. More precisely, we design a computationally efficient algorithm that uses $\widetilde{O}(k/\epsilon^2)$ samples, and learns an arbitrary $k$-SIIRV within error $\epsilon,$ in total variation distance. Moreover, we show that the {\em optimal} sample complexity of this learning problem is $\Theta((k/\epsilon^2)\sqrt{\log(1/\epsilon)}).$ Our algorithm proceeds by learning the Fourier transform of the target $k$-SIIRV in its effective support. Its correctness relies on the {\em approximate sparsity} of the Fourier transform of $k$-SIIRVs -- a structural property that we establish, roughly stating that the Fourier transform of $k$-SIIRVs has small magnitude outside a small set. #R##N#Along the way we prove several new structural results about $k$-SIIRVs. As one of our main structural contributions, we give an efficient algorithm to construct a sparse {\em proper} $\epsilon$-cover for ${\cal S}_{n,k},$ in total variation distance. We also obtain a novel geometric characterization of the space of $k$-SIIRVs. Our characterization allows us to prove a tight lower bound on the size of $\epsilon$-covers for ${\cal S}_{n,k}$, and is the key ingredient in our tight sample complexity lower bound. #R##N#Our approach of exploiting the sparsity of the Fourier transform in distribution learning is general, and has recently found additional applications.

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 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-3 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "list decodable learning via sum of squares"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "hyperplane clustering via dual principal component pursuit"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"

Connected through hyperedge HE-4 (induced by source paper "efficiently learning structured distributions from untrusted batches"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "high dimensional estimation via sum of squares proofs"
    - "federated learning strategies for improving communication efficiency"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning discrete distributions from untrusted batches"
    - "learning populations of parameters"

Connected through hyperedge HE-5 (induced by source paper "a polynomial time algorithm for log concave maximum likelihood via locally exponential families"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "an efficient algorithm for high dimensional log concave maximum likelihood"
    - "density estimation for shift invariant multidimensional distributions"
    - "a polynomial time algorithm for maximum likelihood estimation of multivariate log concave densities"
    - "a size free clt for poisson multinomials and its applications"
    - "efficient robust proper learning of log concave distributions"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"

Connected through hyperedge HE-6 (induced by source paper "learning sums of independent random variables with sparse collective support"):
  with members:
    - "boolean function analysis meets stochastic optimization an approximation scheme for stochastic knapsack"
    - "playing anonymous games using simple strategies"
    - "a size free clt for poisson multinomials and its applications"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "big data on the rise testing monotonicity of distributions"
    - "learning arbitrary statistical mixtures of discrete distributions"

Connected through hyperedge HE-7 (induced by source paper "density estimation for shift invariant multidimensional distributions"):
  with members:
    - "learning sums of independent random variables with sparse collective support"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning multivariate log concave distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "sample optimal density estimation in nearly linear time"
    - "near optimal sample estimators for spherical gaussian mixtures"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

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"
    - "sample optimal density estimation in nearly linear time"

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)