human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "universal deep beamformer for variable rate ultrasound imaging"):
  with members:
    - "sparse convolutional beamforming for ultrasound imaging"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "high quality ultrasonic multi line transmission through deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "high frame rate cardiac ultrasound imaging with deep learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised deep learning algorithm for pde based forward and inverse problems"):
  with members:
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

Connected through hyperedge HE-3 (induced by source paper "learning beamforming in ultrasound imaging"):
  with members:
    - "spatial transformer networks"

Question: Please tell me which class the center node 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)