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: "learning under distributed weak supervision"
  Abstract: The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research.

Connected through hyperedge HE-1 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "data distillation towards omni supervised learning"

Connected through hyperedge HE-3 (induced by source paper "a survey of crowdsourcing in medical image analysis"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "crowd disagreement of medical images is informative"
    - "crowd assisted polyp annotation of virtual colonoscopy videos"
    - "employing weak annotations for medical image analysis problems"
    - "expected exponential loss for gaze based video and volume ground truth annotation"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "negative results in computer vision a perspective"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-4 (induced by source paper "color recognition for rubik s cube robot"):
  with members:
    - "perceptual visual interactive learning"
    - "perceptual uniform descriptor and ranking on manifold a bridge between image representation and ranking for image retrieval"
    - "packing and padding coupled multi index for accurate image retrieval"

Connected through hyperedge HE-5 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

Connected through hyperedge HE-6 (induced by source paper "3d reconstruction in canonical co ordinate space from arbitrarily oriented 2d images"):
  with members:
    - "predicting slice to volume transformation in presence of arbitrary subject motion"
    - "slice to volume medical image registration a survey"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a deep metric for multimodal registration"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "employing weak annotations for medical image analysis problems"):
  with members:
    - "early experiences with crowdsourcing airway annotations in chest ct"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "multi atlas segmentation using partially annotated data methods and annotation strategies"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "deepcut object segmentation from bounding box annotations using convolutional neural networks"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "microsoft coco common objects in context"
    - "fully convolutional multi class multiple instance learning"
    - "from image level to pixel level labeling with convolutional networks"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "theano new features and speed improvements"

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)