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: "votenet a deep learning label fusion method for multi atlas segmentation"
  Abstract: Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computations are fast at test time, and segmentation quality is typically high. In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. In this work, we propose a DL-based label fusion strategy (VoteNet) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach. We also provide an experimental analysis of the upper performance bound achievable by our method. While unlikely achievable in practice, this bound suggests room for further performance improvements. Lastly, to address the runtime disadvantage of standard MAS, all our results make use of a fast DL registration approach.

Connected through hyperedge HE-1 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "one shot neural architecture search via self evaluated template network"
    - "fully convolutional one shot object segmentation for industrial robotics"
    - "learning correspondence from the cycle consistency of time"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "one shot neural architecture search through a posteriori distribution guided sampling"
    - "neural multi atlas label fusion application to cardiac mr images"

Connected through hyperedge HE-2 (induced by source paper "votenet an improved deep learning label fusion method for multi atlas segmentation"):
  with members:
    - "a novel focal tversky loss function with improved attention u net for lesion segmentation"
    - "attention u net learning where to look for the pancreas"
    - "on calibration of modern neural networks"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "multi atlas segmentation of biomedical images a survey"

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)