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: "greed is super a fast algorithm for super resolution"
  Abstract: We present a fast two-phase algorithm for super-resolution with strong theoretical guarantees. Given the low-frequency part of the spectrum of a sequence of impulses, Phase I consists of a greedy algorithm that roughly estimates the impulse positions. These estimates are then refined by local optimization in Phase II. #R##N#In contrast to the convex relaxation proposed by Cand\`es et al., our approach has a low computational complexity but requires the impulses to be separated by an additional logarithmic factor to succeed. The backbone of our work is the fundamental work of Slepian et al. involving discrete prolate spheroidal wave functions and their unique properties.

Connected through hyperedge HE-1 (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"
    - "music for multidimensional spectral estimation stability and super resolution"

Connected through hyperedge HE-2 (induced by source paper "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind demixing and deconvolution at near optimal rate"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-3 (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"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "on landau s eigenvalue theorem and information cut sets"
    - "compressive sensing of analog signals using discrete prolate spheroidal sequences"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-4 (induced by source paper "a geometric analysis of phase retrieval"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "phase retrieval via incremental truncated wirtinger flow"
    - "phaseless rcovery using gauss newton method"

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)