human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "off the grid two dimensional line spectral estimation with prior information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robustness of two dimensional line spectral estimation against spiky noise")
  Member "guaranteed blind sparse spikes deconvolution via lifting and convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "spectral super resolution with prior knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed compressed sensing off the grid")
  Member "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")

Question: Please tell me which class the source paper 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)