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: "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
  Abstract: Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.

Connected through hyperedge HE-1 (induced by source paper "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "visualizing convolutional networks for mri based diagnosis of alzheimer s disease"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-2 (induced by source paper "neuro dram a 3d recurrent visual attention model for interpretable neuroimaging classification"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "end to end alzheimer s disease diagnosis and biomarker identification"
    - "explaining explanations an overview of interpretability of machine learning"
    - "residual and plain convolutional neural networks for 3d brain mri classification"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "transfer learning with intelligent training data selection for prediction of alzheimer s disease"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "deep learning in neural networks"
    - "learning transferable features with deep adaptation networks"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a joint autoencoder for prediction and its application in gps trajectory data"):
  with members:
    - "lstm a search space odyssey"
    - "improving palliative care with deep learning"
    - "tensor representation in high frequency financial data for price change prediction"
    - "convolutional recurrent neural networks for music classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "a convolutional neural network for modelling sentences"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive study of alzheimer s disease classification using convolutional neural networks"):
  with members:
    - "3d inception based cnn with smri and md dti data fusion for alzheimer s disease diagnostics"
    - "towards alzheimer s disease classification through transfer learning"
    - "identity mappings in deep residual networks"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "medical image analysis using convolutional neural networks a review"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "brain tumor segmentation with deep neural networks"
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "medical image retrieval using deep convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "correcting differences in multi site neuroimaging data using generative adversarial networks"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative adversarial text to image synthesis"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "3d inception based cnn with smri and md dti data fusion for alzheimer s disease diagnostics"):
  with members:
    - "3d cnn based classification using smri and md dti images for alzheimer disease studies"
    - "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"
    - "residual and plain convolutional neural networks for 3d brain mri classification"
    - "a survey on deep learning in medical image analysis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "classification of alzheimer s disease structural mri data by deep learning convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)