human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  Abstract: Reconstructing continuous signals from a small number of discrete samples is a fundamental problem across science and engineering. In practice, we are often interested in signals with 'simple' Fourier structure, such as bandlimited, multiband, and Fourier sparse signals. More broadly, any prior knowledge about a signal's Fourier power spectrum can constrain its complexity. Intuitively, signals with more highly constrained Fourier structure require fewer samples to reconstruct. #R##N#We formalize this intuition by showing that, roughly, a continuous signal from a given class can be approximately reconstructed using a number of samples proportional to the *statistical dimension* of the allowed power spectrum of that class. Further, in nearly all settings, this natural measure tightly characterizes the sample complexity of signal reconstruction. #R##N#Surprisingly, we also show that, up to logarithmic factors, a universal non-uniform sampling strategy can achieve this optimal complexity for *any class of signals*. We present a simple and efficient algorithm for recovering a signal from the samples taken. For bandlimited and sparse signals, our method matches the state-of-the-art. At the same time, it gives the first computationally and sample efficient solution to a broad range of problems, including multiband signal reconstruction and kriging and Gaussian process regression tasks in one dimension. #R##N#Our work is based on a novel connection between randomized linear algebra and signal reconstruction with constrained Fourier structure. We extend tools based on statistical leverage score sampling and column-based matrix reconstruction to the approximation of continuous linear operators that arise in signal reconstruction. We believe that these extensions are of independent interest and serve as a foundation for tackling a broad range of continuous time problems using randomized methods.

Includes members:
  Member "relating leverage scores and density using regularized christoffel functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nystrom landmark sampling and regularized christoffel functions")
  Member "random fourier features for kernel ridge regression approximation bounds and statistical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "sublinear time low rank approximation of positive semidefinite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarially robust low dimensional representations")
  Member "active regression via linear sample sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nearly sample optimal sparse fourier transform in any dimension ripless and filterless")
  Member "fourier sparse interpolation without a frequency gap":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "a robust sparse fourier transform in the continuous setting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "principal component projection without principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "over parameterized adversarial training an analysis overcoming the curse of dimensionality")
  Member "recursive sampling for the nystr om method":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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)