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: "a deterministic sub linear time sparse fourier algorithm via non adaptive compressed sensing methods"
  Abstract: We study the problem of estimating the best B term Fourier representation for a given frequency-sparse signal (i.e., vector) $\textbf{A}$ of length $N \gg B$. More explicitly, we investigate how to deterministically identify B of the largest magnitude frequencies of $\hat{\textbf{A}}$, and estimate their coefficients, in polynomial$(B,\log N)$ time. Randomized sub-linear time algorithms which have a small (controllable) probability of failure for each processed signal exist for solving this problem. However, for failure intolerant applications such as those involving mission-critical hardware designed to process many signals over a long lifetime, deterministic algorithms with no probability of failure are highly desirable. In this paper we build on the deterministic Compressed Sensing results of Cormode and Muthukrishnan (CM) \cite{CMDetCS3,CMDetCS1,CMDetCS2} in order to develop the first known deterministic sub-linear time sparse Fourier Transform algorithm suitable for failure intolerant applications. Furthermore, in the process of developing our new Fourier algorithm, we present a simplified deterministic Compressed Sensing algorithm which improves on CM's algebraic compressibility results while simultaneously maintaining their results concerning exponential decay.

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 "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"
    - "the restricted isometry property of subsampled fourier matrices"

Connected through hyperedge HE-3 (induced by source paper "group testing with probabilistic tests theory design and application"):
  with members:
    - "boolean compressed sensing and noisy group testing"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing with probabilistic measurements a group testing solution"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "shannon theoretic limits on noisy compressive 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.DM(Discrete Mathematics)