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: "compressive sensing of analog signals using discrete prolate spheroidal sequences"
  Abstract: Compressive sensing (CS) has recently emerged as a framework for efficiently capturing signals that are sparse or compressible in an appropriate basis. While often motivated as an alternative to Nyquist-rate sampling, there remains a gap between the discrete, finite-dimensional CS framework and the problem of acquiring a continuous-time signal. In this paper, we attempt to bridge this gap by exploiting the Discrete Prolate Spheroidal Sequences (DPSS's), a collection of functions that trace back to the seminal work by Slepian, Landau, and Pollack on the effects of time-limiting and bandlimiting operations. DPSS's form a highly efficient basis for sampled bandlimited functions; by modulating and merging DPSS bases, we obtain a dictionary that offers high-quality sparse approximations for most sampled multiband signals. This multiband modulated DPSS dictionary can be readily incorporated into the CS framework. We provide theoretical guarantees and practical insight into the use of this dictionary for recovery of sampled multiband signals from compressive measurements.

Connected through hyperedge HE-1 (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"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "principal component projection without principal component analysis"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-2 (induced by source paper "the eigenvalue distribution of discrete periodic time frequency limiting operators"):
  with members:
    - "fast algorithms for the computation of fourier extensions of arbitrary length"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"

Connected through hyperedge HE-3 (induced by source paper "roast rapid orthogonal approximate slepian transform"):
  with members:
    - "orthogonal precoding for ultra reliable wireless communication links"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"

Connected through hyperedge HE-4 (induced by source paper "time limited toeplitz operators on abelian groups applications in information theory and subspace approximation"):
  with members:
    - "the eigenvalue distribution of discrete periodic time frequency limiting operators"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "fast algorithms for the computation of fourier extensions of arbitrary length"
    - "greed is super a fast algorithm for super resolution"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "on landau s eigenvalue theorem and information cut sets"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-5 (induced by source paper "deterministic coding theorems for blind sensing optimal measurement rate and fractal dimension"):
  with members:
    - "efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to mri"
    - "on landau s eigenvalue theorem and information cut sets"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "blind compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "mse estimates for multitaper spectral estimation and off grid compressive sensing"):
  with members:
    - "time frequency and time scale analysis of deformed stationary processes with application to non stationary sound modeling"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "fourier bessel rotational invariant eigenimages"

Connected through hyperedge HE-7 (induced by source paper "reconstruction of frequency hopping signals from multi coset samples"):
  with members:
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-8 (induced by source paper "greed is super a fast algorithm for super resolution"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "near minimax line spectral estimation"
    - "super resolution via superset selection and pruning"
    - "a tensor based method for missing traffic data completion"
    - "support detection in super resolution"

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)