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: "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.

Connected through hyperedge HE-1 (induced by source paper "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"):
  with members:
    - "sparse reconstruction from hadamard matrices a lower bound"
    - "estimating the frequency of a clustered signal"
    - "improved lower bounds for the restricted isometry property of subsampled fourier matrices"
    - "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"
    - "dimension independent sparse fourier transform"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "active regression via linear sample sparsification"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "sublinear time algorithms for compressive phase retrieval"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "predicting positive and negative links with noisy queries theory practice"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-3 (induced by source paper "an adaptive sublinear time block sparse fourier transform"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "nearly optimal sparse fourier transform"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "not so random features"):
  with members:
    - "introduction to online convex optimization"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a la carte learning fast kernels"
    - "scalable kernel methods via doubly stochastic gradients"

Connected through hyperedge HE-5 (induced by source paper "fourier sparse interpolation without a frequency gap"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimal approximate matrix product in terms of stable rank"
    - "super resolution off the grid"
    - "sketching as a tool for numerical linear algebra"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "near minimax line spectral estimation"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-6 (induced by source paper "a sparse multidimensional fft for real positive vectors"):
  with members:
    - "fast and efficient sparse 2d discrete fourier transform using sparse graph codes"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"

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)