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 adaptation of 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 proposed 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 CADDementia MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset.

Connected through hyperedge HE-1 (induced by source paper "machine learning on biomedical images interactive learning transfer learning class imbalance and beyond"):
  with members:
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "attention u net learning where to look for the pancreas"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "focal loss for dense object detection"
    - "towards alzheimer s disease classification through transfer learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-2 (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"
    - "deep learning in neural networks"
    - "learning transferable features with deep adaptation networks"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural 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-3 (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 a deeply supervised adaptable 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"

Connected through hyperedge HE-4 (induced by source paper "neuroimaging modality fusion in alzheimer s classification using convolutional neural networks"):
  with members:
    - "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "going deeper with convolutions"
    - "multidimensional classification of hippocampal shape features discriminates alzheimer s disease and mild cognitive impairment from normal aging"

Connected through hyperedge HE-5 (induced by source paper "end to end alzheimer s disease diagnosis and biomarker identification"):
  with members:
    - "a survey on deep learning in medical image analysis"

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)