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 "3d roi aware u net for accurate and efficient colorectal tumor segmentation"):
  Abstract: Segmentation of colorectal cancerous regions from Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. To address this important yet challenging task within the framework of performance-leading deep learning methods, regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness and reduction of false positives. Distinct from sliding window or discrete localization-segmentation based models, we propose a novel multi-task framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and intra-RoI segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level feature maps from the backbone network to form a GPU memory-efficient decoder for detail-preserving intra-RoI segmentation. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the promising efficiency gains demonstrated by the proposed method, we went on to ensemble multiple models to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available.

Includes members:
  Member "estimation of the volume of the left ventricle from mri images using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning in cardiology")
  Member "path aggregation network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "crossbar net a novel convolutional network for kidney tumor segmentation in ct images":
    also connected through other hyperedges:
      (none)
  Member "segmentation by detection a cascade network for volumetric medical image segmentation":
    also connected through other hyperedges:
      (none)
  Member "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional networks with dense connectivity")
  Member "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)