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: "music for multidimensional spectral estimation stability and super resolution"
  Abstract: This paper presents a performance analysis of the MUltiple SIgnal Classification (MUSIC) algorithm applied on   $D$   dimensional single-snapshot spectral estimation while   $s$    true frequencies are located on the continuum of a bounded domain. Inspired by the matrix pencil form, we construct a D-fold Hankel matrix from the measurements and exploit its Vandermonde decomposition in the noiseless case. MUSIC amounts to identifying a noise subspace, evaluating a noise-space correlation function, and localizing frequencies by searching the   $s$    smallest local minima of the noise-space correlation function. In the noiseless case,   $(2s)^{D}$    measurements guarantee an exact reconstruction by MUSIC as the noise-space correlation function vanishes exactly at true frequencies. When noise exists, we provide an explicit estimate on the perturbation of the noise-space correlation function in terms of noise level, dimension   $D$  , the minimum separation among frequencies, the maximum and minimum amplitudes while frequencies are separated by 2 Rayleigh Length (RL) at each direction. As a by-product the maximum and minimum non-zero singular values of the multidimensional Vandermonde matrix whose nodes are on the unit sphere are estimated under a gap condition of the nodes. Under the 2-RL separation condition, if noise is i.i.d. Gaussian, we show that perturbation of the noise-space correlation function decays like   $\sqrt{\log(\#({\bf N}))/\#({\bf N})}$   as the sample size   $\#({\bf N})$   increases. When the separation among frequencies drops below 2 RL, our numerical experiments show that the noise tolerance of MUSIC obeys a power law with the minimum separation of frequencies.

Connected through hyperedge HE-1 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "elementary l error estimates for super resolution de noising"
    - "super resolution of positive sources the discrete setup"
    - "the recoverability limit for superresolution via sparsity"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "super resolution from noisy data"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-2 (induced by source paper "stable super resolution of images a theoretical study"):
  with members:
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "greed is super a fast algorithm for super resolution"

Connected through hyperedge HE-3 (induced by source paper "robust recovery of positive stream of pulses"):
  with members:
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "super resolution of positive sources the discrete setup"
    - "support recovery for sparse deconvolution of positive measures"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "the recoverability limit for superresolution via sparsity"

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)