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: "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
  Abstract: Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions. Most existing approaches for this problem are based on multi-atlas label fusion strategies, which are time-consuming and sensitive to registration errors. As alternative to these methods, we propose a hyper-densely connected 3D convolutional neural network that employs MR-T1 and T2 images as input, which are processed independently in two separated paths. An important difference with previous densely connected networks is the use of direct connections between layers from the same and different paths. Adopting such dense connectivity helps the learning process by including deep supervision and improving gradient flow. We evaluated our approach on data from the MICCAI Grand Challenge on 6-month infant Brain MRI Segmentation (iSEG), obtaining very competitive results. Among 21 teams, our approach ranked first or second in most metrics, translating into a state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "hyperdense net a hyper densely connected cnn for multi modal image segmentation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "multi scale dense networks for resource efficient image classification"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities"):
  with members:
    - "multi modal convolutional neural networks for brain tumor segmentation"
    - "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"
    - "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-3 (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"
    - "multi modal convolutional neural networks for brain tumor segmentation"
    - "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"
    - "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)