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 vessel segmentation by learning graphical connectivity"
  Abstract: We propose a novel deep-learning-based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without considering the graphical structure of vessel shape. To address this, we incorporate a graph convolutional network into a unified CNN architecture, where the final segmentation is inferred by combining the different types of features. The proposed method can be applied to expand any type of CNN-based vessel segmentation method to enhance the performance. Experiments show that the proposed method outperforms the current state-of-the-art methods on two retinal image datasets as well as a coronary artery X-ray angiography dataset.

Connected through hyperedge HE-1 (induced by source paper "graph attention network based pruning for reconstructing 3d liver vessel morphology from contrasted ct images"):
  with members:
    - "graph neural network for interpreting task fmri biomarkers"
    - "on the convergence of adam and beyond"
    - "fast graph representation learning with pytorch geometric"
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "a novel deep learning pipeline for retinal vessel detection in fluorescein angiography"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "unet a nested u net architecture for medical image segmentation"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "graph refinement based tree extraction using mean field networks and graph neural networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "extracting tree structures in ct data by tracking multiple statistically ranked hypotheses"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "extraction of airways with probabilistic state space models and bayesian smoothing"
    - "gated graph sequence neural networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "deep residual learning for image recognition"

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)