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: "frangi net a neural network approach to vessel segmentation"
  Abstract: In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to $17\%$ in F1 score.

Connected through hyperedge HE-1 (induced by source paper "learning with known operators reduces maximum training error bounds"):
  with members:
    - "pyro nn python reconstruction operators in neural networks"
    - "visual object networks image generation with disentangled 3d representation"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "user loss a forced choice inspired approach to train neural networks directly by user interaction"
    - "deriving neural network architectures using precision learning parallel to fan beam conversion"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "a gentle introduction to deep learning in medical image processing"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-2 (induced by source paper "a gentle introduction to deep learning in medical image processing"):
  with members:
    - "towards arbitrary noise augmentation deep learning for sampling from arbitrary probability distributions"
    - "metric driven learning of correspondence weighting for 2 d 3 d image registration"
    - "adversarial patch"
    - "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
    - "image reconstruction via variational network for real time hand held sound speed imaging"
    - "deriving neural network architectures using precision learning parallel to fan beam conversion"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

Connected through hyperedge HE-3 (induced by source paper "deriving neural network architectures using precision learning parallel to fan beam conversion"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "precision learning towards use of known operators in neural networks"
    - "precision learning reconstruction filter kernel discretization"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "user loss a forced choice inspired approach to train neural networks directly by user interaction"):
  with members:
    - "precision learning towards use of known operators in neural networks"
    - "precision learning reconstruction filter kernel discretization"

Connected through hyperedge HE-5 (induced by source paper "precision learning towards use of known operators in neural networks"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a guided spatial transformer network for histology cell differentiation"
    - "quasi quantile sparse image prior for spatio temporal denoising of retinal oct data"
    - "precision learning reconstruction filter kernel discretization"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

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)