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: "few shot 3d multi modal medical image segmentation using generative adversarial learning"
  Abstract: We address the problem of segmenting 3D multi-modal medical images in scenarios where very few labeled examples are available for training. Leveraging the recent success of adversarial learning for semi-supervised segmentation, we propose a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images. The proposed method prevents over-fitting by learning to discriminate between true and fake patches obtained by a generator network. Our work extends current adversarial learning approaches, which focus on 2D single-modality images, to the more challenging context of 3D volumes of multiple modalities. The proposed method is evaluated on the problem of segmenting brain MRI from the iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement is reported, compared to state-of-art segmentation networks trained in a fully-supervised manner. In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches. Our code is publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "semi supervised few shot learning for medical image segmentation"):
  with members:
    - "on the texture bias for few shot cnn segmentation"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "boosting few shot visual learning with self supervision"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

Connected through hyperedge HE-2 (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"
    - "semi supervised deep learning for fully convolutional networks"
    - "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-3 (induced by source paper "learn to segment organs with a few bounding boxes"):
  with members:
    - "squeeze excite guided few shot segmentation of volumetric images"
    - "unet a nested u net architecture for medical image segmentation"
    - "squeeze and excitation networks"
    - "one shot learning for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "network in network"

Connected through hyperedge HE-4 (induced by source paper "difficulty aware meta learning for rare disease diagnosis"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "automated skin lesion classification using ensemble of deep neural networks in isic 2018 skin lesion analysis towards melanoma detection challenge"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)