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 "interactive segmentation of medical images through fully convolutional neural networks"):
  Abstract: Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. Manual segmentation offers full control over the quality of the results, but is tedious, time consuming and prone to operator bias. Fully automated methods require no human effort, but often deliver sub-optimal results without providing users with the means to make corrections. Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction. In this paper we present a deep learning (DL) based semi-automated segmentation approach that aims to be a "smart" interactive tool for region of interest delineation in medical images. We demonstrate its use for segmenting multiple organs on computed tomography (CT) of the abdomen. Our approach solves some of the most pressing clinical challenges: (i) it requires only one to a few user clicks to deliver excellent 2D segmentations in a fast and reliable fashion; (ii) it can generalize to previously unseen structures and "corner cases"; (iii) it delivers results that can be corrected quickly in a smart and intuitive way up to an arbitrary degree of precision chosen by the user and (iv) ensures high accuracy. We present our approach and compare it to other techniques and previous work to show the advantages brought by our method.

Includes members:
  Member "deepigeos a deep interactive geodesic framework for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis")
  Member "a large annotated medical image dataset for the development and evaluation of segmentation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the future of digital health with federated learning")
  Member "interactive full image segmentation by considering all regions jointly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "nnu net self adapting framework for u net based medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  Member "deep extreme cut from extreme points to object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "dltk state of the art reference implementations for deep learning on medical images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation")
  Member "3d u net learning dense volumetric segmentation from sparse annotation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional occupancy 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)