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: "visual explanations from deep 3d convolutional neural networks for alzheimer s disease classification"
  Abstract: We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.

Connected through hyperedge HE-1 (induced by source paper "understanding patch based learning by explaining predictions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "why should i trust you explaining the predictions of any classifier"
    - "wavenet a generative model for raw audio"
    - "understanding neural networks through deep visualization"
    - "action recognition using visual attention"

Connected through hyperedge HE-2 (induced by source paper "global model interpretation via recursive partitioning"):
  with members:
    - "gradient boosted feature selection"
    - "network dissection quantifying interpretability of deep visual representations"
    - "places an image database for deep scene understanding"
    - "why should i trust you explaining the predictions of any classifier"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "binary classification of alzheimer disease using smri imaging modality and deep learning"):
  with members:
    - "an ensemble of deep convolutional neural networks for alzheimer s disease detection and classification"
    - "towards alzheimer s disease classification through transfer learning"
    - "xception deep learning with depthwise separable convolutions"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural networks"

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)