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 "universal deep beamformer for variable rate ultrasound imaging"):
  Abstract: Ultrasound (US) imaging is based on the time-reversal principle, in which individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented as a delay-and-sum (DAS) beamformer, the image quality quickly degrades as the number of measurement channels decreases. To address this problem, various types of adaptive beamforming techniques have been proposed using predefined models of the signals. However, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate. Here, we demonstrate for the first time that a single universal deep beamformer trained using a purely data-driven way can generate significantly improved images over widely varying aperture and channel subsampling patterns. In particular, we design an end-to-end deep learning framework that can directly process sub-sampled RF data acquired at different subsampling rate and detector configuration to generate high quality ultrasound images using a single beamformer. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.

Includes members:
  Member "a deep learning framework for single sided sound speed inversion in medical ultrasound":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised deep learning algorithm for pde based forward and inverse problems")
  Member "sparse convolutional beamforming for ultrasound imaging":
    also connected through other hyperedges:
      (none)
  Member "deep residual learning for accelerated mri using magnitude and phase networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "high quality ultrasonic multi line transmission through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end learning based ultrasound reconstruction")
  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 frame rate cardiac ultrasound imaging with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high quality ultrasonic multi line transmission through deep learning")
  Member "framing u net via deep convolutional framelets application to sparse view ct":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "learning when and where to zoom with deep reinforcement learning")

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)