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 "deterministic sparse fourier transform with an ell_infty guarantee"):
  Abstract: In this paper we revisit the deterministic version of the Sparse Fourier Transform problem. While the randomized case is well-understood, the main work in the deterministic case is the work of Merhi et al. (J Fourier Anal Appl 2018), which obtains O(k^2 log^{5.5} n) samples and similar runtime with the \ell_2/\ell_1 guarantee. We focus on the stronger \ell_\infty/\ell_1 guarantee and the closely related problem of incoherent matrices. We list our contributions as follows. 1. We give a "for-all" scheme with O(k^2 log n) samples. 2. We give a for-all scheme with O(k^2 log^2 n) samples, and O(k^2 log^3 n) time. 3. We derandomize both schemes in polynomial time in n, such that all subsequent Sparse Fourier Transform "queries" can be answered deterministically in O(nk log n) and O(k^2 log^3 n) time, respectively. 4. We give two different deterministic constructions of incoherent matrices, combinatorial objects that are closely related to \ell_infty/\ell_1 sparse recovery schemes. The first one keeps rows of the Discrete Fourier Matrix, while the second uses Fourier-friendly measurements with the help of the Weil bound from algebraic geometry. Our constructions match previous constructions by DeVore (J Complexity 2007), Amini and Marvasti (IEEE Trans Info Theory 2011) and Nelson, Nguyen and Woodruff (RANDOM 12), where there was no constraint on the form of the incoherent matrix. Our algorithms are nearly sample optimal, since a lower bound of {\Omega}(k^2 + k log n) is known, even for the case where the sensing matrix can be arbitrarily designed. Similarly, for incoherent matrices, a lower bound of {\Omega}(k^2 log n/ log k) is known, indicating that our constructions are nearly optimal.

Includes members:
  Member "dimension independent sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "a universal sampling method for reconstructing signals with simple fourier transforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "oblivious sketching of high degree polynomial kernels")
  Member "deterministic heavy hitters with sublinear query time":
    also connected through other hyperedges:
      (none)
  Member "an adaptive sublinear time block sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "sample efficient estimation and recovery in sparse fft via isolation on average":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy preserving learning via deep net pruning")
  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 "the restricted isometry property of subsampled fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "one shot neural architecture search via compressive sensing")

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)