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 "brain tumor segmentation on mri with missing modalities"):
  Abstract: Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis. However, rather than having complete four modalities as in BraTS dataset, it is common to have missing modalities in clinical scenarios. We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path. We use self-supervised training through channel dropout and also propose a novel domain adaptation method on feature maps to recover the information from the missing channel. Our results demonstrate that the quality of the segmentation depends on which modality is missing. Furthermore, we also discuss and visualize the contribution of each modality to the segmentation results. Their contributions are along well with the expert screening routine.

Includes members:
  Member "deep recurrent level set for segmenting brain tumors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform")
  Member "deep depth completion of a single rgb d image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")
  Member "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation")
  Member "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dilated multi fiber network for real time brain tumor segmentation in mri")
  Member "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation")
  Member "brain tumor segmentation with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "hemis hetero modal image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)