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 modality adaptive method for segmenting brain tumors and organs at risk in radiation therapy planning"):
  Abstract: In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

Includes members:
  Member "neuronet fast and robust reproduction of multiple brain image segmentation pipelines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d whole brain segmentation using spatially localized atlas network tiles")
  Member "domain adaptation for mri organ segmentation using reverse classification accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks")
  Member "brain tumor segmentation with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "error corrective boosting for learning fully convolutional networks with limited data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fatsegnet a fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon mri")
  Member "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "medgan medical image translation using gans")
  Member "transfer learning for domain adaptation in mri application in brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "hemis hetero modal image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion")

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)