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: "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
  Abstract: Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the \emph{ADNI} MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy and robustness. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the \emph{CADDementia} dataset.

Connected through hyperedge HE-1 (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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "diagnosis of alzheimer s disease via multi modality 3d convolutional neural network"):
  with members:
    - "reproducible evaluation of classification methods in alzheimer s disease framework and application to mri and pet data"
    - "learn to pay attention"
    - "a survey on deep learning in medical image analysis"
    - "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "asd diagnet a hybrid learning approach for detection of autism spectrum disorder using fmri data"):
  with members:
    - "3d convolutional neural networks for classification of functional connectomes"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "automatic detection of adhd and asd from expressive behaviour in rgbd data"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "understanding data augmentation for classification when to warp"
    - "multimodal deep learning for robust rgb d object recognition"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (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"
    - "deep residual learning for image recognition"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "applications of deep learning and reinforcement learning to biological data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "brain tumor segmentation with deep neural networks"
    - "deep reinforcement learning an overview"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep learning trends for focal brain pathology segmentation in mri"
    - "medical image denoising using convolutional denoising autoencoders"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "deepmirgene deep neural network based precursor microrna prediction"

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 "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"
    - "classification of alzheimer s disease structural mri data by deep learning convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "visualizing convolutional networks for mri based diagnosis of alzheimer s disease"):
  with members:
    - "3d cnn based classification using smri and md dti images for alzheimer disease studies"
    - "methods for interpreting and understanding deep neural networks"
    - "residual and plain convolutional neural networks for 3d brain mri classification"
    - "a survey on deep learning in medical image analysis"
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "striving for simplicity the all convolutional net"
    - "visualizing and understanding convolutional 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.LG(Machine Learning)