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: "discrete uncertainty principles and sparse signal processing"
  Abstract: We develop new discrete uncertainty principles in terms of numerical sparsity, which is a continuous proxy for the 0-norm. Unlike traditional sparsity, the continuity of numerical sparsity naturally accommodates functions which are nearly sparse. After studying these principles and the functions that achieve exact or near equality in them, we identify certain consequences in a number of sparse signal processing applications.

Connected through hyperedge HE-1 (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-2 (induced by source paper "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "improved lower bounds for the restricted isometry property of subsampled fourier matrices"):
  with members:
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-4 (induced by source paper "sparse recovery in wigner d basis expansion"):
  with members:
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-5 (induced by source paper "the restricted isometry property of subsampled fourier matrices"):
  with members:
    - "the list decoding size of fourier sparse boolean functions"
    - "computational complexity of certifying restricted isometry property"
    - "on model based rip 1 matrices"
    - "on the list decodability of random linear codes with large error rates"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

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.IT(Information Theory)