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: "semi supervised deep learning for fully convolutional networks"
  Abstract: Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.

Connected through hyperedge HE-1 (induced by source paper "deep co training for semi supervised image segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "deeply supervised salient object detection with short connections"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"
    - "deep co training for semi supervised image recognition"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a multi task u net for segmentation with lazy labels"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "coarse to fine semantic segmentation from image level labels"
    - "a two stage 3d unet framework for multi class segmentation on full resolution image"
    - "deep learning with mixed supervision for brain tumor segmentation"
    - "coarse to fine volumetric segmentation of teeth in cone beam ct"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "revisiting cyclegan for semi supervised segmentation"):
  with members:
    - "deep co training for semi supervised image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised domain adaptation for medical imaging segmentation with self ensembling"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "curriculum semi supervised segmentation"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "few shot 3d multi modal medical image segmentation using generative adversarial learning"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (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"
    - "deep semi supervised segmentation with weight averaged consistency targets"
    - "multi task learning for left atrial segmentation on ge mri"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"

Connected through hyperedge HE-6 (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"
    - "deep semi supervised segmentation with weight averaged consistency targets"
    - "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-7 (induced by source paper "semi supervised brain lesion segmentation with an adapted mean teacher model"):
  with members:
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "temporal ensembling for semi supervised learning"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "multi layer domain adaptation for deep convolutional networks"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "training medical image analysis systems like radiologists"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "squeeze and excitation networks"
    - "improved training of wasserstein gans"

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)