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: "3d densely convolutional networks for volumetric segmentation"
  Abstract: In the isointense stage, the accurate volumetric image segmentation is a challenging task due to the low contrast between tissues. In this paper, we propose a novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation. The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network. By concatenating features map of fine and coarse dense blocks, it allows capturing multi-scale contextual information. Experimental results demonstrate significant advantages of the proposed method over existing methods, in terms of both segmentation accuracy and parameter efficiency in MICCAI grand challenge on 6-month infant brain MRI segmentation.

Connected through hyperedge HE-1 (induced by source paper "scaling distributed training of flood filling networks on hpc infrastructure for brain mapping"):
  with members:
    - "an empirical model of large batch training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-2 (induced by source paper "discriminative consistent domain generation for semi supervised learning"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
    - "domain invariant adversarial learning for unsupervised domain adaption"
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"):
  with members:
    - "segmentation of liver lesions with reduced complexity deep models"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "2d densely connected convolution neural networks for automatic liver and tumor segmentation"
    - "attention u net learning where to look for the pancreas"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "detection aided liver lesion segmentation using deep learning"
    - "attention is all you need"
    - "survivalnet predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3d convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "exclusive independent probability estimation using deep 3d fully convolutional densenets application to isointense infant brain mri segmentation"):
  with members:
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a 3d coarse to fine framework for volumetric medical image segmentation"):
  with members:
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"
    - "brain tumor segmentation with deep neural networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "spatial aggregation of holistically nested networks for automated pancreas 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)