human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi modal convolutional neural networks for brain tumor segmentation"
  Abstract: In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.

Connected through hyperedge HE-1 (induced by source paper "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities"):
  with members:
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"):
  with members:
    - "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Question: Please tell me which class the center node 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)