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 semi supervised learning with uncertainty aware multi view co training"
  Abstract: We propose a novel framework, uncertainty-aware multi-view co-training (UMCT), to address semi-supervised learning on 3D data, such as volumetric data in medical imaging. The original co-training method was applied to non-visual data. It requires different sources, or representations, of the data, which are called different views and differ from viewpoint in computer vision. Co-training was recently applied to visual tasks where the views were deep networks learnt by adversarial training. In our work, targeted at 3D data, co-training is achieved by exploiting multi-viewpoint consistency. We generate different views by rotating the 3D data and utilize asymmetrical 3D kernels to further encourage diversified features of each sub-net. In addition, we propose an uncertainty-aware attention mechanism to estimate the reliability of each view prediction with Bayesian deep learning. As one view requires the supervision from other views in co-training, our self-adaptive approach computes a confidence score for the prediction of each unlabeled sample, in order to assign a reliable pseudo label and thus achieve better performance. We show the effectiveness of our proposed method on several open datasets from medical image segmentation tasks (NIH pancreas & LiTS liver tumor dataset). A method based on our approach achieved the state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework even when fully supervised training is feasible.

Connected through hyperedge HE-1 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "scalable neural architecture search for 3d medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "thickened 2d networks for efficient 3d medical image segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "long term feature banks for detailed video understanding"
    - "compact generalized non local network"
    - "differentiable learning to normalize via switchable normalization"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"

Connected through hyperedge HE-3 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "attention u net learning where to look for the pancreas"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"
    - "hi fi hierarchical feature integration for skeleton detection"

Connected through hyperedge HE-5 (induced by source paper "on defending against label flipping attacks on malware detection systems"):
  with members:
    - "can machine learning model with static features be fooled an adversarial machine learning approach"
    - "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
    - "label propagation for deep semi supervised learning"
    - "is feature selection secure against training data poisoning"
    - "learning to reweight examples for robust deep learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "data poisoning attacks against online learning"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-6 (induced by source paper "fusionnet incorporating shape and texture for abnormality detection in 3d abdominal ct scans"):
  with members:
    - "joint shape representation and classification for detecting pdac"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-7 (induced by source paper "multi task attention based semi supervised learning for medical image segmentation"):
  with members:
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "semi supervised learning with ladder 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)