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: "parcellation of visual cortex on high resolution histological brain sections using convolutional neural networks"
  Abstract: Microscopic analysis of histological sections is considered the “gold standard” to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2µm resolution. It is based on a convolutional neural network that combines topological information from probabilistic atlases with the texture features learned from high-resolution cell-body stained images. The model is applied to visual areas and trained on a sparse set of partial annotations. We show how predictions are transferable to new brains and spatially consistent across sections.

Connected through hyperedge HE-1 (induced by source paper "probabilistic atlases to enforce topological constraints"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "efficient video object segmentation via network modulation"
    - "improving cytoarchitectonic segmentation of human brain areas with self supervised siamese networks"
    - "residual attention network for image classification"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "improving cytoarchitectonic segmentation of human brain areas with self supervised siamese networks"):
  with members:
    - "siamese regression networks with efficient mid level feature extraction for 3d object pose estimation"
    - "unsupervised visual representation learning by context prediction"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised learning of visual representations using videos"
    - "deep neural networks for anatomical brain segmentation"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "almost instant brain atlas segmentation for large scale studies"):
  with members:
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "end to end learning of brain tissue segmentation from imperfect labeling"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)