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: "quicknat segmenting mri neuroanatomy in 20 seconds"
  Abstract: Whole brain segmentation from structural magnetic resonance imaging is a prerequisite for most morphological analyses, but requires hours of processing time and therefore delays the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolution neural network that segments a brain scan in 20 seconds. To enable training of the complex network with limited annotated data, we propose to pre-train on auxiliary labels created from existing segmentation software and to subsequently fine-tune on manual labels. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior performance to state-of-the-art methods, while being about 700 times faster. This drastic speed up greatly facilitates the processing of large data repositories and supports the translation of imaging biomarkers by making them almost instantaneously available.

Connected through hyperedge HE-1 (induced by source paper "psacnn pulse sequence adaptive fast whole brain segmentation"):
  with members:
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "pulse sequence resilient fast brain segmentation"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-2 (induced by source paper "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "towards integrating spatial localization in convolutional neural networks for brain image segmentation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "joint inference on structural and diffusion mri for sequence adaptive bayesian segmentation of thalamic nuclei with probabilistic atlases"):
  with members:
    - "multi atlas segmentation of biomedical images a survey"

Connected through hyperedge HE-4 (induced by source paper "knowing what you know in brain segmentation using bayesian deep neural networks"):
  with members:
    - "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"
    - "end to end learning of brain tissue segmentation from imperfect labeling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "on calibration of modern neural networks"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "densely connected convolutional networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "distributed weight consolidation a brain segmentation case study"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "competition vs concatenation in skip connections of fully convolutional networks"):
  with members:
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "on the importance of normalisation layers in deep learning with piecewise linear activation units"
    - "understanding locally competitive networks"

Connected through hyperedge HE-8 (induced by source paper "concurrent spatial and channel squeeze excitation in fully convolutional networks"):
  with members:
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "squeeze and excitation networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "fully convolutional networks for semantic 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)