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: "end to end learning of brain tissue segmentation from imperfect labeling"
  Abstract: Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many potentially faulty intermediate steps. In order to streamline the segmentation, we introduce a deep learning model that is based on volumetric dilated convolutions, subsequently reducing both processing time and errors. Compared to its competitors, the model has a reduced set of parameters and thus is easier to train and much faster to execute. The contrast in performance between the dilated network and its competitors becomes obvious when both are tested on a large dataset of unprocessed human brain volumes. The dilated network consistently outperforms not only another state-of-the-art deep learning approach, the up convolutional network, but also the ground truth on which it was trained. Not only can the incredible speed of our model make large scale analyses much easier but we also believe it has great potential in a clinical setting where, with little to no substantial delay, a patient and provider can go over test results.

Connected through hyperedge HE-1 (induced by source paper "segmentation of the proximal femur from mr images using deep convolutional neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cnn based segmentation of medical imaging data"
    - "fastventricle cardiac segmentation with enet"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "knowing what you know in brain segmentation using bayesian deep neural networks"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"
    - "on calibration of modern neural networks"
    - "almost instant brain atlas segmentation for large scale studies"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "robustly representing uncertainty in deep neural networks through sampling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "distributed weight consolidation a brain segmentation case study"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "almost instant brain atlas segmentation for large scale studies"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting in neural networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "almost instant brain atlas segmentation for large scale studies"):
  with members:
    - "parcellation of visual cortex on high resolution histological brain sections using convolutional neural networks"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "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)