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: "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks"
  Abstract: Over the past decade, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is power learning machine learning algorithm in classification while extracting high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.

Connected through hyperedge HE-1 (induced by source paper "dc al gan pseudoprogression and true tumor progression of glioblastoma multiform image classification based on dcgan and alexnet"):
  with members:
    - "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"
    - "generative adversarial networks an overview"
    - "generative adversarial networks and probabilistic graph models for hyperspectral image classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "synthetic medical images from dual generative adversarial networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "improved techniques for training gans"

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"
    - "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"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "neuroimaging modality fusion in alzheimer s classification using convolutional neural networks"):
  with members:
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "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-4 (induced by source paper "predicting cognitive decline with deep learning of brain metabolism and amyloid imaging"):
  with members:
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "going deeper with convolutions"
    - "striving for simplicity the all convolutional net"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-5 (induced by source paper "alzheimer s disease diagnostics by adaptation of 3d convolutional network"):
  with members:
    - "learning transferable features with deep adaptation networks"
    - "net2net accelerating learning via knowledge transfer"
    - "how transferable are features in deep neural networks"
    - "winner take all autoencoders"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"
    - "multidimensional classification of hippocampal shape features discriminates alzheimer s disease and mild cognitive impairment from normal aging"

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)