human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neuroimaging modality fusion in alzheimer s classification using convolutional neural networks"):
  Abstract: Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and PET, but a comprehensive and balanced comparison of these modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

Includes members:
  Member "alzheimer s disease diagnostics by adaptation of 3d convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning on biomedical images interactive learning transfer learning class imbalance and beyond")
  Member "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dc al gan pseudoprogression and true tumor progression of glioblastoma multiform image classification based on dcgan and alexnet")
  Member "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "multidimensional classification of hippocampal shape features discriminates alzheimer s disease and mild cognitive impairment from normal aging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a wide and deep neural network for survival analysis from anatomical shape and tabular clinical data")

Question: Please tell me which class the source paper 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)