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: "almost instant brain atlas segmentation for large scale studies"
  Abstract: Large scale studies of group differences in healthy controls and patients and screenings for early stage disease prevention programs require processing and analysis of extensive multisubject datasets. Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions. Current automatic approaches are time-consuming and hardly scalable; they often involve many error prone intermediate steps and don't utilize other available modalities. To alleviate these problems, we propose a feedforward fully convolutional neural network trained on the output produced by the state of the art models. Incredible speed due to available powerful GPUs neural network makes this analysis much easier and faster (from $>10$ hours to a minute). The proposed model is more than two orders of magnitudes faster than the state of the art and yet as accurate. We have evaluated the network's performance by comparing it with the state of the art in the task of differentiating region volumes of healthy controls and patients with schizophrenia on a dataset with 311 subjects. This comparison provides a strong evidence that speed did not harm the accuracy. The overall quality may also be increased by utilizing multi-modal datasets (not an easy task for other models) by simple adding more modalities as an input. Our model will be useful in large-scale studies as well as in clinical care solutions, where it can significantly reduce delay between the patient screening and the result.

Connected through hyperedge HE-1 (induced by source paper "knowing what you know in brain segmentation using bayesian deep neural networks"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"
    - "on calibration of modern neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "robustly representing uncertainty in deep neural networks through sampling"
    - "end to end learning of brain tissue segmentation from imperfect labeling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "distributed weight consolidation a brain segmentation case study"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting in neural networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "end to end learning of brain tissue segmentation from imperfect labeling"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"

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)