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 "deep learning in rf sub sampled b mode ultrasound imaging"):
  Abstract: In portable, three dimensional, and ultra-fast ultrasound (US) imaging systems, there is an increasing need to reconstruct high quality images from a limited number of RF data from receiver (Rx) or scan-line (SC) sub-sampling. However, due to the severe side lobe artifacts from RF sub-sampling, the standard beam-former often produces blurry images with less contrast that are not suitable for diagnostic purpose. To address this problem, some researchers have studied compressed sensing (CS) to exploit the sparsity of the image or RF data in some domains. However, the existing CS approaches require either hardware changes or computationally expensive algorithms. To overcome these limitations, here we propose a novel deep learning approach that directly interpolates the missing RF data by utilizing redundancy in the Rx-SC plane. In particular, the network design principle derives from a novel interpretation of the deep neural network as a cascaded convolution framelets that learns the data-driven bases for Hankel matrix decomposition. Our extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate without sacrificing the image quality.

Includes members:
  Member "deep residual learning for accelerated mri using magnitude and phase networks":
    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 "k space deep learning for accelerated mri":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding geometry of encoder decoder cnns")
  Member "image reconstruction by domain transform manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound")
  Member "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "deep learning for accelerated ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep convolutional neural network for inverse problems in imaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")

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)