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: "fourier sparse interpolation without a frequency gap"
  Abstract: We consider the problem of estimating a Fourier-sparse signal from noisy samples, where the sampling is done over some interval $[0, T]$ and the frequencies can be "off-grid". Previous methods for this problem required the gap between frequencies to be above 1/T, the threshold required to robustly identify individual frequencies. We show the frequency gap is not necessary to estimate the signal as a whole: for arbitrary $k$-Fourier-sparse signals under $\ell_2$ bounded noise, we show how to estimate the signal with a constant factor growth of the noise and sample complexity polynomial in $k$ and logarithmic in the bandwidth and signal-to-noise ratio. #R##N#As a special case, we get an algorithm to interpolate degree $d$ polynomials from noisy measurements, using $O(d)$ samples and increasing the noise by a constant factor in $\ell_2$.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "list decodable learning via sum of squares"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "hyperplane clustering via dual principal component pursuit"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"

Connected through hyperedge HE-3 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"

Connected through hyperedge HE-4 (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-5 (induced by source paper "deterministic sparse fourier transform with an ell_infty guarantee"):
  with members:
    - "dimension independent sparse fourier transform"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "deterministic heavy hitters with sublinear query time"
    - "an adaptive sublinear time block sparse fourier transform"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "a robust sparse fourier transform in the continuous setting"
    - "the restricted isometry property of subsampled fourier matrices"
    - "discrete uncertainty principles and sparse signal processing"

Connected through hyperedge HE-6 (induced by source paper "reconstruction under outliers for fourier sparse functions"):
  with members:
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "efficient algorithms for outlier robust regression"
    - "robust polynomial regression up to the information theoretic limit"
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "analysis of boolean functions"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"

Connected through hyperedge HE-7 (induced by source paper "estimating the frequency of a clustered signal"):
  with members:
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "active regression via linear sample sparsification"
    - "a robust sparse fourier transform in the continuous setting"

Connected through hyperedge HE-8 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  with members:
    - "relating leverage scores and density using regularized christoffel functions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "active regression via linear sample sparsification"
    - "a robust sparse fourier transform in the continuous setting"
    - "principal component projection without principal component analysis"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"

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)