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 "compressive mri quantification using convex spatiotemporal priors and deep auto encoders"):
  Abstract: We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact auto-encoder network with residual blocks in order to embed Bloch manifold projections through multiscale piecewise affine approximations, and to replace the nonscalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols.

Includes members:
  Member "deep mr fingerprinting with total variation and low rank subspace priors":
    also connected through other hyperedges:
      (none)
  Member "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep mr fingerprinting with total variation and low rank subspace priors")
  Member "magnetic resonance fingerprinting using recurrent neural networks":
    also connected through other hyperedges:
      (none)
  Member "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep mr fingerprinting with total variation and low rank subspace priors")
  Member "geometry of deep learning for magnetic resonance fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep mr fingerprinting with total variation and low rank subspace priors")
  Member "better than real complex valued neural nets for mri fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep mr fingerprinting with total variation and low rank subspace priors")
  Member "mr fingerprinting deep reconstruction network drone":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "separation of water and fat signal in whole body gradient echo scans using convolutional neural networks")
  Member "deep kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effective diversity in population based reinforcement learning")

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.CV(Computer Vision and Pattern Recognition)