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: "the restricted isometry property of subsampled fourier matrices"
  Abstract: A matrix $A \in \mathbb{C}^{q \times N}$ satisfies the restricted isometry property of order $k$ with constant $\varepsilon$ if it preserves the $\ell_2$ norm of all $k$-sparse vectors up to a factor of $1\pm \varepsilon$. We prove that a matrix $A$ obtained by randomly sampling $q = O(k \cdot \log^2 k \cdot \log N)$ rows from an $N \times N$ Fourier matrix satisfies the restricted isometry property of order $k$ with a fixed $\varepsilon$ with high probability. This improves on Rudelson and Vershynin (Comm. Pure Appl. Math., 2008), its subsequent improvements, and Bourgain (GAFA Seminar Notes, 2014).

Connected through hyperedge HE-1 (induced by source paper "one shot neural architecture search via compressive sensing"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "learnable embedding space for efficient neural architecture compression"
    - "evaluating the search phase of neural architecture search"
    - "massively parallel hyperparameter tuning"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (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"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "discrete uncertainty principles and sparse signal processing"

Connected through hyperedge HE-3 (induced by source paper "derandomized compressed sensing with nonuniform guarantees for 120001 1 recovery"):
  with members:
    - "kesten mckay law for random subensembles of paley equiangular tight frames"
    - "random subsets of structured deterministic frames have manova spectra"
    - "average case hardness of rip certification"
    - "living on the edge phase transitions in convex programs with random data"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"
    - "kirkman equiangular tight frames and codes"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-4 (induced by source paper "faster johnson lindenstrauss transforms via kronecker products"):
  with members:
    - "oblivious sketching of high degree polynomial kernels"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "optimality of the johnson lindenstrauss lemma"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "a joint graph based coding scheme for the unsourced random access gaussian channel"):
  with members:
    - "near gaussian multiple access channel capacity detection and decoding"
    - "capacity of gaussian many access channels"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"):
  with members:
    - "discrete uncertainty principles and sparse signal processing"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "near optimal sample complexity bounds for circulant binary embedding"

Connected through hyperedge HE-8 (induced by source paper "uniform recovery bounds for structured random matrices in corrupted compressed sensing"):
  with members:
    - "modulated unit norm tight frames for compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "convolutional compressed sensing using deterministic sequences"
    - "the restricted isometry property for random block diagonal matrices"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"

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)