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: "deep residual network for off resonance artifact correction with application to pediatric body magnetic resonance angiography with 3d cones"
  Abstract: Purpose: Off-resonance artifact correction by deep-learning, to facilitate rapid pediatric body imaging with a scan time efficient 3D cones trajectory. Methods: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of 30 pediatric magnetic resonance angiography exams. Each exam acquired a short-readout scan (1.18 ms +- 0.38) and a long-readout scan (3.35 ms +- 0.74) at 3T. Short-readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long-readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared to short-readout scans by normalized root-mean-square error (NRMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scans were also compared by scoring on eight anatomical features by two radiologists, using analysis of variance with post-hoc Tukey's test. Reader agreement was determined with intraclass correlation. Results: Long-readout scans were on average 59.3% shorter than short-readout scans. Images from Off-ResNet had superior NRMSE, SSIM, and PSNR compared to uncorrected images across +-1kHz off-resonance (P<0.01). The proposed method had superior NRMSE over -677Hz to +1kHz and superior SSIM and PSNR over +-1kHz compared to autofocus (P<0.01). Radiologic scoring demonstrated that long-readout scans corrected with Off-ResNet were non-inferior to short-readout scans (P<0.01). Conclusion: The proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a non-inferior image quality compared to diagnostically standard short-readout scans.

Connected through hyperedge HE-1 (induced by source paper "prostate segmentation from ultrasound images using residual fully convolutional network"):
  with members:
    - "fine grained recurrent neural networks for automatic prostate segmentation in ultrasound images"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.CV(Computer Vision and Pattern Recognition)