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 new ensemble learning framework for 3d biomedical image segmentation"):
  Abstract: 3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the base-learners as multiple versions of "ground truths". Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semi-supervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.

Includes members:
  Member "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "ensembling neural networks for digital pathology images classification and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bach grand challenge on breast cancer histology images")
  Member "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "suggestive annotation a deep active learning framework for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforced active learning for image segmentation")
  Member "multi stage multi recursive input fully convolutional networks for neuronal boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boundary aware feature propagation for scene segmentation")
  Member "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning in cardiology")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ismo gan adversarial learning for monocular non rigid 3d reconstruction")

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)