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: "two dimensional super resolution via convex relaxation"
  Abstract: In this paper, we address the problem of recovering point sources from two-dimensional low-pass measurements, which is known as the super-resolution problem. This is the fundamental concern of many applications such as electronic imaging, optics, microscopy, and line spectral estimations. We assume that the point sources are located in the square [0,1]#N# 2#N# with unknown locations and complex amplitudes. The only available information is low-pass Fourier measurements band limited to the integer square [-f#N# c#N#, f#N# c#N#]#N# 2#N#. The sources are estimated by minimizing the total variation norm, which leads to a convex optimization problem. We observe in numerical results that if the sources are closer than 1.4/f#N# c#N#, they might not be recovered by the proposed optimization. We theoretically show that there exists a dual certificate which guarantees exact recovery when the sources are separated by at least 1.68/f#N# c#N#, reducing the gap between the available theoretical guarantee for the source separations and the observed results in the simulations.

Connected through hyperedge HE-1 (induced by source paper "robustness of two dimensional line spectral estimation against spiky noise"):
  with members:
    - "off the grid two dimensional line spectral estimation with prior information"
    - "generalized line spectral estimation via convex optimization"
    - "robust spectral compressed sensing via structured matrix completion"
    - "spectral super resolution with prior knowledge"
    - "super resolution sparse mimo ofdm channel estimation based on spatial and temporal correlations"
    - "atomic norm denoising with applications to line spectral estimation"
    - "super resolution from noisy data"
    - "towards a mathematical theory of 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)