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: "deepigeos a deep interactive geodesic framework for medical image segmentation"
  Abstract: Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

Connected through hyperedge HE-1 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "annotation cost minimization for medical image segmentation using suggestive mixed supervision fully convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "large scale interactive object segmentation with human annotators"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep grabcut for object selection"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised segmentation from extreme points"):
  with members:
    - "interactive segmentation of medical images through fully convolutional neural networks"
    - "constrained cnn losses for weakly supervised segmentation"
    - "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist"
    - "self learning to detect and segment cysts in lung ct images without manual annotation"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "learning to segment medical images with scribble supervision alone"
    - "deep extreme cut from extreme points to object segmentation"

Connected through hyperedge HE-4 (induced by source paper "iteratively refined interactive 3d medical image segmentation with multi agent reinforcement learning"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "unet a nested u net architecture for medical image segmentation"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "iterative interaction training for segmentation editing networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "annotating object instances with a polygon rnn"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "interactive segmentation of medical images through fully convolutional neural networks"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "interactive full image segmentation by considering all regions jointly"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "deep extreme cut from extreme points to object segmentation"
    - "dltk state of the art reference implementations for deep learning on medical images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "a semi automated usability evaluation framework for interactive image segmentation systems"):
  with members:
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "swipecut interactive segmentation with diversified seed proposals"
    - "robust seed mask generation for interactive image segmentation"
    - "ui net interactive artificial neural networks for iterative image segmentation based on a user model"
    - "deep interactive object selection"

Connected through hyperedge HE-7 (induced by source paper "interactive image segmentation using label propagation through complex networks"):
  with members:
    - "building networks for image segmentation using particle competition and cooperation"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"

Connected through hyperedge HE-8 (induced by source paper "guide me interacting with deep networks"):
  with members:
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "real time user guided image colorization with learned deep priors"
    - "film visual reasoning with a general conditioning layer"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "rethinking atrous convolution for semantic image segmentation"

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)