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: "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
  Abstract: Given an $n$-length input signal $\mbf{x}$, it is well known that its Discrete Fourier Transform (DFT), $\mbf{X}$, can be computed in $O(n \log n)$ complexity using a Fast Fourier Transform (FFT). If the spectrum $\mbf{X}$ is exactly $k$-sparse (where $k<<n$), can we do better? We show that asymptotically in $k$ and $n$, when $k$ is sub-linear in $n$ (precisely, $k \propto n^{\delta}$ where $0 < \delta <1$), and the support of the non-zero DFT coefficients is uniformly random, we can exploit this sparsity in two fundamental ways (i) {\bf {sample complexity}}: we need only $M=rk$ deterministically chosen samples of the input signal $\mbf{x}$ (where $r < 4$ when $0 < \delta < 0.99$); and (ii) {\bf {computational complexity}}: we can reliably compute the DFT $\mbf{X}$ using $O(k \log k)$ operations, where the constants in the big Oh are small and are related to the constants involved in computing a small number of DFTs of length approximately equal to the sparsity parameter $k$. Our algorithm succeeds with high probability, with the probability of failure vanishing to zero asymptotically in the number of samples acquired, $M$.

Connected through hyperedge HE-1 (induced by source paper "an adaptive sublinear time block sparse fourier transform"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "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-2 (induced by source paper "sample efficient estimation and recovery in sparse fft via isolation on average"):
  with members:
    - "an adaptive sublinear time block sparse fourier transform"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-3 (induced by source paper "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "the restricted isometry property of subsampled fourier matrices"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "nearly optimal sparse fourier transform"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "lower bounds for sparse recovery"
    - "a probabilistic and ripless theory of compressed sensing"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-4 (induced by source paper "fast and robust compressive phase retrieval with sparse graph codes"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "a generalized ldpc framework for robust and sublinear compressive sensing"):
  with members:
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "efficient sketches for the set query problem"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-6 (induced by source paper "a sparse multidimensional fft for real positive vectors"):
  with members:
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "fast and efficient sparse 2d discrete fourier transform using sparse graph codes"
    - "sample optimal average case sparse fourier transform in two dimensions"

Connected through hyperedge HE-7 (induced by source paper "spright a fast and robust framework for sparse walsh hadamard transform"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "a fast hadamard transform for signals with sub linear sparsity in the transform domain"
    - "polarization of the r enyi information dimension for single and multi terminal analog compression"
    - "nearly optimal sparse fourier transform"

Connected through hyperedge HE-8 (induced by source paper "saffron a fast efficient and robust framework for group testing based on sparse graph codes"):
  with members:
    - "learning immune defectives graph through group tests"
    - "nonadaptive group testing with random set of defectives via constant weight codes"
    - "grotesque noisy group testing quick and efficient"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

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)