human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a multi task u net for segmentation with lazy labels"):
  Abstract: The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation. In this paper, we propose a deep convolutional neural network for multi-class segmentation that circumvents this problem by being trainable on coarse data labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy 'lazy' labels. Image segmentation is then stratified into three connected tasks: rough detection of class instances, separation of wrongly connected objects without a clear boundary, and pixel-wise segmentation to find the accurate boundaries of each object. These problems are integrated into a multitask learning framework and the model is trained end-to-end in a semi-supervised fashion. The method is applied on a dataset of food microscopy images. We show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of the annotated data. This allows more flexibility and efficiency for training deep neural networks that are data hungry in a practical setting where manual annotation is expensive, by collecting more lazy (rough) annotations than precisely segmented images.

Includes members:
  Member "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly and semi supervised detection in medical imaging via deep dual branch net")
  Member "coarse to fine semantic segmentation from image level labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "saliency guided self attention network for weakly and semi supervised semantic segmentation")
  Member "a two stage 3d unet framework for multi class segmentation on full resolution image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cfun combining faster r cnn and u net network for efficient whole heart segmentation")
  Member "deep learning with mixed supervision for brain tumor segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mixed supervised dual network for medical image segmentation")
  Member "coarse to fine volumetric segmentation of teeth in cone beam ct":
    also connected through other hyperedges:
      (none)
  Member "multiclass weighted loss for instance segmentation of cluttered cells":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images")
  Member "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")

Question: Please tell me which class the source paper 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)