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 "an adaptive sublinear time block sparse fourier transform"):
  Abstract: The problem of approximately computing the $k$ dominant Fourier coefficients of a vector $X$ quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in algorithms with $O(k\log n\log (n/k))$ runtime [Hassanieh et al., STOC'12] and $O(k\log n)$ sample complexity [Indyk et al., FOCS'14]. These results are proved using non-adaptive algorithms, and the latter $O(k\log n)$ sample complexity result is essentially the best possible under the sparsity assumption alone. #R##N#This paper revisits the sparse FFT problem with the added twist that the sparse coefficients approximately obey a $(k_0,k_1)$-block sparse model. In this model, signal frequencies are clustered in $k_0$ intervals with width $k_1$ in Fourier space, where $k= k_0k_1$ is the total sparsity. Signals arising in applications are often well approximated by this model with $k_0\ll k$. #R##N#Our main result is the first sparse FFT algorithm for $(k_0, k_1)$-block sparse signals with the sample complexity of $O^*(k_0k_1 + k_0\log(1+ k_0)\log n)$ at constant signal-to-noise ratios, and sublinear runtime. A similar sample complexity was previously achieved in the works on model-based compressive sensing using random Gaussian measurements, but used $\Omega(n)$ runtime. To the best of our knowledge, our result is the first sublinear-time algorithm for model based compressed sensing, and the first sparse FFT result that goes below the $O(k\log n)$ sample complexity bound. #R##N#Our algorithm crucially uses {\em adaptivity} to achieve the improved sample complexity bound, and we prove that adaptivity is in fact necessary if Fourier measurements are used: Any non-adaptive algorithm must use $\Omega(k_0k_1\log \frac{n}{k_0k_1})$ samples for the $(k_0,k_1$)-block sparse model, ruling out improvements over the vanilla sparsity assumption.

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 "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nearly sample optimal sparse fourier transform in any dimension ripless and filterless")
  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 "sample efficient estimation and recovery in sparse fft via isolation on average")
  Member "group sparse model selection hardness and relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning based compressive subsampling")
  Member "on model based rip 1 matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "nearly optimal sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "1 eps approximate sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stronger l2 l2 compressed sensing without iterating")

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)