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 "plug and play methods for magnetic resonance imaging"):
  Abstract: Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. But, compared to other clinical imaging modalities (e.g., CT or ultrasound), the data acquisition process for MRI is inherently slow. Furthermore, dynamic applications demand collecting a series of images in quick succession. As a result, reducing acquisition time and improving imaging quality for undersampled datasets have been active areas of research for the last two decades. The combination of parallel imaging and compressive sensing (CS) has been shown to benefit a wide range of MRI applications. More recently, deep learning techniques have been shown to outperform CS methods. Some of these techniques pose the MRI reconstruction as a direct inversion problem and tackle it by training a deep neural network (DNN) to map from the measured Fourier samples and the final image. Considering that the forward model in MRI changes from one dataset to the next, such methods have to be either trained over a large and diverse corpus of data or limited to a specific application, and even then they cannot ensure data consistency. An alternative is to use "plug-and-play" (PnP) algorithms, which iterate image denoising with forward-model based signal recovery. PnP algorithms are an excellent fit for compressive MRI because they decouple image modeling from the forward model, which can change significantly among different scans due to variations in the coil sensitivity maps, sampling patterns, and image resolution. Consequently, with PnP, state-of-the-art image-denoising techniques, such as those based on DNNs, can be directly exploited for compressive MRI image reconstruction. The objective of this article is two-fold: i) to review recent advances in plug-and-play methods, and ii) to discuss their application to compressive MRI image reconstruction.

Includes members:
  Member "bilinear recovery using adaptive vector amp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "plug in estimation in high dimensional linear inverse problems a rigorous analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "an online plug and play algorithm for regularized image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "image reconstruction by domain transform manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "high quality automatic foreground extraction using consensus equilibrium":
    also connected through other hyperedges:
      (none)
  Member "learning proximal operators using denoising networks for regularizing inverse imaging problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bilinear recovery using adaptive vector amp")

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)