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: "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"
  Abstract: We demonstrate that sub-wavelength optical images borne on partially-spatially-incoherent light can be recovered, from their far-field or from the blurred image, given the prior knowledge that the image is sparse, and only that. The reconstruction method relies on the recently demonstrated sparsity-based sub-wavelength imaging. However, for partially-spatially-incoherent light, the relation between the measurements and the image is quadratic, yielding non-convex measurement equations that do not conform to previously used techniques. Consequently, we demonstrate new algorithmic methodology, referred to as quadratic compressed sensing, which can be applied to a range of other problems involving information recovery from partial correlation measurements, including when the correlation function has local dependencies. Specifically for microscopy, this method can be readily extended to white light microscopes with the additional knowledge of the light source spectrum.

Connected through hyperedge HE-1 (induced by source paper "phase retrieval from binary measurements"):
  with members:
    - "undersampled sparse phase retrieval via majorization minimization"
    - "low rank phase retrieval"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "gespar efficient phase retrieval of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-2 (induced by source paper "quantization aware phase retrieval"):
  with members:
    - "phase retrieval from binary measurements"
    - "undersampled sparse phase retrieval via majorization minimization"
    - "low rank phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-3 (induced by source paper "on fienup methods for sparse phase retrieval"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "gespar efficient phase retrieval of sparse signals"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-4 (induced by source paper "fourier phase retrieval uniqueness and algorithms"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on fienup methods for sparse phase retrieval"
    - "on signal reconstruction from frog measurements"
    - "on the 2d phase retrieval problem"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-5 (induced by source paper "sparsity based super resolution for sem images"):
  with members:
    - "image super resolution using deep convolutional networks"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-6 (induced by source paper "on the 2d phase retrieval problem"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-7 (induced by source paper "non convex phase retrieval from stft measurements"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on fienup methods for sparse phase retrieval"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"

Connected through hyperedge HE-8 (induced by source paper "stft phase retrieval uniqueness guarantees and recovery algorithms"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval an overview of recent developments"
    - "sparse phase retrieval from short time fourier measurements"
    - "quadratic basis pursuit"
    - "phase retrieval with application to optical imaging"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "gespar efficient phase retrieval of sparse signals"

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)