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 "mmfnet a multi modality mri fusion network for segmentation of nasopharyngeal carcinoma"):
  Abstract: Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one single decoder to fuse them and obtain high-level features for NPC segmentation. A fusion block is presented to effectively fuse features from multi-modality MRI. It firstly recalibrates low-level features captured from modality-specific encoders to highlight both informative features and regions of interest, then fuses weighted features by a residual fusion block to keep balance between fused ones and high-level features from decoder. Moreover, a training strategy named self-transfer, which utilizes pre-trained modality-specific encoders to initialize multi-encoder-based network, is proposed to make full mining of information from different modalities of MRI. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments.

Includes members:
  Member "self supervised model adaptation for multimodal semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "3d roi aware u net for accurate and efficient colorectal tumor segmentation":
    also connected through other hyperedges:
      (none)
  Member "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "label driven weakly supervised learning for multimodal deformarle image registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "voxelmorph a learning framework for deformable medical image registration")
  Member "cbam convolutional block attention module":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "hyperdense net a hyper densely connected cnn for multi modal image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")

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)