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: "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
  Abstract: In this paper, we consider the extensively studied problem of computing a $k$-sparse approximation to the $d$-dimensional Fourier transform of a length $n$ signal. Our algorithm uses $O(k \log k \log n)$ samples, is dimension-free, operates for any universe size, and achieves the strongest $\ell_\infty/\ell_2$ guarantee, while running in a time comparable to the Fast Fourier Transform. In contrast to previous algorithms which proceed either via the Restricted Isometry Property or via filter functions, our approach offers a fresh perspective to the sparse Fourier Transform problem.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "the state of sparsity in deep neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

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"
    - "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"
    - "fourier sparse interpolation without a frequency gap"

Connected through hyperedge HE-3 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

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)