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: "deeper image quality transfer training low memory neural networks for 3d images"
  Abstract: In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $ - $ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by $ 13\% $. Our code is publicly available.

Connected through hyperedge HE-1 (induced by source paper "reversible gans for memory efficient image to image translation"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "lifting layers analysis and applications"
    - "neural ordinary differential equations"
    - "i revnet deep invertible networks"
    - "the reversible residual network backpropagation without storing activations"
    - "progressive growing of gans for improved quality stability and variation"
    - "the numerics of gans"

Connected through hyperedge HE-2 (induced by source paper "a partially reversible u net for memory efficient volumetric image segmentation"):
  with members:
    - "group normalization"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "i revnet deep invertible networks"
    - "the reversible residual network backpropagation without storing activations"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "multi stage prediction networks for data harmonization"):
  with members:
    - "spherical harmonic residual network for diffusion signal harmonization"
    - "progressive growing of gans for improved quality stability and variation"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "convolutional pose machines"
    - "holistically nested edge detection"

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)