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: "spinal cord gray matter segmentation using deep dilated convolutions"
  Abstract: Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation for medical imaging segmentation with self ensembling"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks"
    - "group normalization"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "taskonomy disentangling task transfer learning"
    - "deep learning in medical image analysis and multimodal learning for clinical decision support 4th international workshop dlmia 2018 and 8th international workshop ml cds 2018 held in conjunction with miccai 2018 granada spain september 20 2018 proceedings"
    - "dida disentangled synthesis for domain adaptation"
    - "deep semi supervised segmentation with weight averaged consistency targets"

Connected through hyperedge HE-2 (induced by source paper "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks"):
  with members:
    - "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "domain adaptation for mri organ segmentation using reverse classification accuracy"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "semi supervised deep learning for fully convolutional networks"
    - "the reversible residual network backpropagation without storing activations"

Connected through hyperedge HE-3 (induced by source paper "deep semi supervised segmentation with weight averaged consistency targets"):
  with members:
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "semi supervised deep learning for fully convolutional networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "a survey on deep learning in medical image analysis"
    - "transferable semi supervised semantic segmentation"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "dental pathology detection in 3d cone beam ct"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "a survey on deep learning in medical image analysis"
    - "detection of tooth caries in bitewing radiographs using deep learning"
    - "medical image synthesis with context aware generative adversarial networks"
    - "densely connected convolutional networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "spinal cord gray matter white matter segmentation on magnetic resonance amira images with md gru"):
  with members:
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"

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)