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: "geometry of deep learning for magnetic resonance fingerprinting"
  Abstract: Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. In this paper we study a deep learning approach to address these shortcomings. Coupled with a dimensionality reduction first layer, the proposed MRF-Net is able to reconstruct quantitative maps by saving more than 60 times in memory and computations required for a DM baseline. Fine-grid manifold enumeration i.e. the MRF dictionary is only used for training the network and not during image reconstruction. We show that the MRF-Net provides a piece-wise affine approximation to the Bloch response manifold projection and that rather than memorizing the dictionary, the network efficiently clusters this manifold and learns a set of hierarchical matched-filters for affine regression of the NMR characteristics in each segment.

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"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "mr fingerprinting deep reconstruction network drone"
    - "deep kernel learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep mr fingerprinting with total variation and low rank subspace priors"):
  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"

Connected through hyperedge HE-3 (induced by source paper "magnetic resonance fingerprinting using recurrent neural networks"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "comparative study of cnn and rnn for natural language processing"
    - "mr fingerprinting deep reconstruction network drone"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "mrf denoising with compressed sensing and adaptive filtering"

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.LG(Machine Learning)