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 "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"):
  Abstract: We consider the problem of computing a $k$-sparse approximation to the Fourier transform of a length $N$ signal. Our main result is a randomized algorithm for computing such an approximation (i.e. achieving the $\ell_2/\ell_2$ sparse recovery guarantees using Fourier measurements) using $O_d(k\log N\log\log N)$ samples of the signal in time domain that runs in time $O_d(k\log^{d+3} N)$, where $d\geq 1$ is the dimensionality of the Fourier transform. The sample complexity matches the lower bound of $\Omega(k\log (N/k))$ for non-adaptive algorithms due to \cite{DIPW} for any $k\leq N^{1-\delta}$ for a constant $\delta>0$ up to an $O(\log\log N)$ factor. Prior to our work a result with comparable sample complexity $k\log N \log^{O(1)}\log N$ and sublinear runtime was known for the Fourier transform on the line \cite{IKP}, but for any dimension $d\geq 2$ previously known techniques either suffered from a polylogarithmic factor loss in sample complexity or required $\Omega(N)$ runtime.

Includes members:
  Member "a robust sparse fourier transform in the continuous setting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "sample optimal average case sparse fourier transform in two dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a fast face detection method via convolutional neural network")
  Member "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an adaptive sublinear time block sparse fourier transform")
  Member "nearly optimal sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "restricted isometry of fourier matrices and list decodability of random linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "querying a matrix through matrix vector products")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

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)