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: "a compressed sensing framework for magnetic resonance fingerprinting"
  Abstract: Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are: a random pulse excitation sequence following the MRF technique; a random EPI subsampling strategy and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that theoretically, as long as the excitation sequence possesses an appropriate form of persistent excitation, we are able to accurately recover the proton density, T1, T2 and off-resonance maps simultaneously from a limited number of samples. These results are further supported through extensive simulations using a brain phantom.

Connected through hyperedge HE-1 (induced by source paper "compressive mri quantification using convex spatiotemporal priors and deep auto encoders"):
  with members:
    - "deep mr fingerprinting with total variation and low rank subspace priors"
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "magnetic resonance fingerprinting using recurrent neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "mr fingerprinting deep reconstruction network drone"
    - "deep kernel learning"

Connected through hyperedge HE-2 (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"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "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-3 (induced by source paper "geometry of deep learning for magnetic resonance fingerprinting"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "geometric deep learning going beyond euclidean data"
    - "mr fingerprinting deep reconstruction network drone"
    - "efficient representation of low dimensional manifolds using deep networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "coupled dictionary learning for multi contrast mri reconstruction"):
  with members:
    - "a deep information sharing network for multi contrast compressed sensing mri reconstruction"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "bayesian nonparametric dictionary learning for compressed sensing mri"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-5 (induced by source paper "symmetry reduction for dynamic programming"):
  with members:
    - "the invariant extended kalman filter as a stable observer"

Connected through hyperedge HE-6 (induced by source paper "compressed quantitative mri bloch response recovery through iterated projection"):
  with members:
    - "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)