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: "mr fingerprinting deep reconstruction network drone"
  Abstract: PURPOSE: Demonstrate a novel fast method for reconstruction of multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods. #R##N#METHODS: A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed using the Bloch equations. The accuracy of the NN reconstruction of noisy data is compared to conventional MRF template matching as a function of training data size, and quantified in a both simulated numerical brain phantom data and acquired data from the ISMRM/NIST phantom. The utility of the method is demonstrated in a healthy subject in vivo at 1.5 T. #R##N#RESULTS: Network training required 10 minutes and once trained, data reconstruction required approximately 10 ms. Reconstruction of simulated brain data using the NN resulted in a root-mean-square error (RMSE) of 3.5 ms for T1 and 7.8 ms for T2. The RMSE for the NN trained on sparse dictionaries was approximately 6 fold lower for T1 and 2 fold lower for T2 than conventional MRF dot-product dictionary matching on the same dictionaries. Phantom measurements yielded good agreement (R2=0.99) between the T1 and T2 estimated by the NN and reference values from the ISMRM/NIST phantom. #R##N#CONCLUSION: Reconstruction of MRF data with a NN is accurate, 300 fold faster and more robust to noise and undersampling than conventional MRF dictionary matching.

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"
    - "deep kernel learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "separation of water and fat signal in whole body gradient echo scans using convolutional neural networks"):
  with members:
    - "fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water fat mri"
    - "image reconstruction by domain transform manifold learning"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning designed rf pulse deeprf slr"):
  with members:
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "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-5 (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"
    - "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-6 (induced by source paper "magnetic resonance fingerprinting using recurrent neural networks"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "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"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "mrf denoising with compressed sensing and adaptive filtering"

Connected through hyperedge HE-7 (induced by source paper "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "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)