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 "fastmri an open dataset and benchmarks for accelerated mri"):
  Abstract: Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.

Includes members:
  Member "image reconstruction by domain transform manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "framing u net via deep convolutional framelets application to sparse view ct":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound")
  Member "a transfer learning approach for accelerated mri using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalising deep learning mri reconstruction across different domains")
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic pyramid for image generation")
  Member "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the future of digital health with federated learning")
  Member "inversenet solving inverse problems with splitting networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "a deep cascade of convolutional neural networks for mr image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "admm net a deep learning approach for compressive sensing mri":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tree search network for sparse regression")

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)