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: "the fourier transform of poisson multinomial distributions and its algorithmic applications"
  Abstract: An $(n, k)$-Poisson Multinomial Distribution (PMD) is a random variable of the form $X = \sum_{i=1}^n X_i$, where the $X_i$'s are independent random vectors supported on the set of standard basis vectors in $\mathbb{R}^k.$ In this paper, we obtain a refined structural understanding of PMDs by analyzing their Fourier transform. As our core structural result, we prove that the Fourier transform of PMDs is {\em approximately sparse}, i.e., roughly speaking, its $L_1$-norm is small outside a small set. By building on this result, we obtain the following applications: #R##N#{\bf Learning Theory.} We design the first computationally efficient learning algorithm for PMDs with respect to the total variation distance. Our algorithm learns an arbitrary $(n, k)$-PMD within variation distance $\epsilon$ using a near-optimal sample size of $\widetilde{O}_k(1/\epsilon^2),$ and runs in time $\widetilde{O}_k(1/\epsilon^2) \cdot \log n.$ Previously, no algorithm with a $\mathrm{poly}(1/\epsilon)$ runtime was known, even for $k=3.$ #R##N#{\bf Game Theory.} We give the first efficient polynomial-time approximation scheme (EPTAS) for computing Nash equilibria in anonymous games. For normalized anonymous games with $n$ players and $k$ strategies, our algorithm computes a well-supported $\epsilon$-Nash equilibrium in time $n^{O(k^3)} \cdot (k/\epsilon)^{O(k^3\log(k/\epsilon)/\log\log(k/\epsilon))^{k-1}}.$ The best previous algorithm for this problem had running time $n^{(f(k)/\epsilon)^k},$ where $f(k) = \Omega(k^{k^2})$, for any $k>2.$ #R##N#{\bf Statistics.} We prove a multivariate central limit theorem (CLT) that relates an arbitrary PMD to a discretized multivariate Gaussian with the same mean and covariance, in total variation distance. Our new CLT strengthens the CLT of Valiant and Valiant by completely removing the dependence on $n$ in the error bound.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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"
    - "properly learning poisson binomial distributions in almost polynomial time"

Connected through hyperedge HE-5 (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"
    - "big data on the rise testing monotonicity of distributions"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "properly learning poisson binomial distributions in almost polynomial time"

Connected through hyperedge HE-6 (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"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "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-7 (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"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"

Connected through hyperedge HE-8 (induced by source paper "fourier based testing for families of distributions"):
  with members:
    - "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"
    - "pseudorandomness via the discrete fourier transform"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "testing shape restrictions 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)