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 "cnn based projected gradient descent for consistent ct image reconstruction"):
  Abstract: We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements. We propose a relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images. We show that this algorithm is guaranteed to converge and, under certain conditions, converges to a local minimum of a non-convex inverse problem. Finally, we propose a simple scheme to train the CNN to act like a projector. Our experiments on sparse-view computed-tomography reconstruction show an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique.

Includes members:
  Member "efficient sum of outer products dictionary learning soup dil and its application to inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of fast structured dictionary learning")
  Member "deep learning with domain adaptation for accelerated projection reconstruction mr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image restoration using total variation regularized deep image prior")
  Member "one network to solve them all solving linear inverse problems using deep projection models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proximal splitting networks for image restoration")
  Member "deep learning for photoacoustic tomography from sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully dense unet for 2 d sparse photoacoustic tomography artifact removal")
  Member "recurrent inference machines for solving inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural enhanced belief propagation on factor graphs")
  Member "skip connections as effective symmetry breaking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "the little engine that could regularization by denoising red":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "plug and play admm for image restoration fixed point convergence and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blind image restoration without prior knowledge")

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)