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: "deep semi supervised segmentation with weight averaged consistency targets"
  Abstract: Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. In this work, we expand the Mean Teacher approach to segmentation tasks and show that it can bring important improvements in a realistic small data regime using a publicly available multi-center dataset from the Magnetic Resonance Imaging (MRI) domain. We also devise a method to solve the problems that arise when using traditional data augmentation strategies for segmentation tasks on our new training scheme.

Connected through hyperedge HE-1 (induced by source paper "consistency regularization and cutmix for semi supervised semantic segmentation"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "universal semi supervised semantic segmentation"
    - "s4 net geometry consistent semi supervised semantic segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "self ensembling for visual domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "uncertainty aware self ensembling model for semi supervised 3d left atrium segmentation"):
  with members:
    - "semi supervised brain lesion segmentation with an adapted mean teacher model"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "multi task learning for left atrial segmentation on ge mri"
    - "semi supervised deep learning for fully convolutional networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"

Connected through hyperedge HE-3 (induced by source paper "semi supervised medical image segmentation via learning consistency under transformations"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "improving the segmentation of anatomical structures in chest radiographs using u net with an imagenet pre trained encoder"
    - "semi supervised deep learning for fully convolutional networks"
    - "improving landmark localization with semi supervised learning"
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (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"
    - "spinal cord gray matter segmentation using deep dilated convolutions"

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)