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: "compressive source separation theory and methods for hyperspectral imaging"
  Abstract: We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements. We also propose efficient optimization algorithms and perform extensive experimentation on synthetic and real datasets, which reveals that our approach can be used to encode HSI with far less measurements and computational effort than traditional compressive sensing methods.

Connected through hyperedge HE-1 (induced by source paper "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"):
  with members:
    - "better than real complex valued neural nets for mri fingerprinting"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "mr fingerprinting deep reconstruction network drone"
    - "a compressed sensing framework for magnetic resonance fingerprinting"
    - "sparse subspace clustering algorithm theory and applications"
    - "model based compressive sensing"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "inexact gradient projection and fast data driven compressed sensing"):
  with members:
    - "sharp time data tradeoffs for linear inverse problems"
    - "matrix recipes for hard thresholding methods"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "noise folding in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "hyperspectral image recovery via hybrid regularization"):
  with members:
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-4 (induced by source paper "generalized inpainting method for hyperspectral image acquisition"):
  with members:
    - "a parallel compressive imaging architecture for one shot acquisition"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "hyperspectral image recovery from incomplete and imperfect measurements via hybrid regularization"):
  with members:
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-6 (induced by source paper "compressive imaging and characterization of sparse light deflection maps"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "spread spectrum magnetic resonance imaging"
    - "theory and applications of compressed sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressive schlieren deflectometry"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-7 (induced by source paper "a compressed sensing framework for magnetic resonance fingerprinting"):
  with members:
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "on variable density compressive sampling"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

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)