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: "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
  Abstract: Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dictionary matching. We evaluate our method on multiparametric brain scans and compare it to three recent MRF reconstruction approaches. Our method achieves state-of-the-art reconstruction accuracy and yields qualitatively more appealing maps compared to other reconstruction methods. In addition, the reconstruction time is significantly reduced compared to a dictionary-based approach.

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 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"
    - "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:
    - "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"

Connected through hyperedge HE-3 (induced by source paper "geometry of deep learning for magnetic resonance fingerprinting"):
  with members:
    - "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"
    - "a compressed sensing framework for magnetic resonance fingerprinting"

Connected through hyperedge HE-4 (induced by source paper "magnetic resonance fingerprinting using recurrent neural networks"):
  with members:
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "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.CV(Computer Vision and Pattern Recognition)