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 "a deep learning framework for single sided sound speed inversion in medical ultrasound"):
  Abstract: Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates. Motivated by research and theory showing that pressure wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from a conventional ultrasound probe, enabling elasticity based diagnostics using portable and low-cost devices. Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of large volumes of ground truth data. Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at super real-time frame rates. Our method shows exceptional results on simulated data and highly encouraging initial results on real data. Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies. Significance: High-end ultrasound devices remain inaccessible in many locations. Utilizing pressure sound speed and deep learning technologies brings the same quality diagnostic abilities to low power devices at real-time frame rates. High potential frame rates also enable dynamic functional imaging, impossible with shear wave imaging.

Includes members:
  Member "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "medgan medical image translation using gans")
  Member "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "medgan medical image translation using gans")
  Member "a benchmark for breast ultrasound image segmentation busis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fuzzy semantic segmentation of breast ultrasound image with breast anatomy constraints")
  Member "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "framing u net via deep convolutional framelets application to sparse view ct":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level wavelet convolutional neural networks")
  Member "brain tumor segmentation with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "towards ct quality ultrasound imaging using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "high quality ultrasonic multi line transmission through deep learning")
  Member "deep learning in rf sub sampled b mode ultrasound imaging":
    also connected through other hyperedges:
      (none)

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.LG(Machine Learning)