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 "binary classification of alzheimer disease using smri imaging modality and deep learning"):
  Abstract: Alzheimer Disease (AD) is the most common form of dementia affecting the elderly population worldwide. Many neuroimaging modalities have been used to check the detection and progression of AD of which structural Magnetic Resonance Imaging (sMRI) is an important one. The recent rise in the popularity of deep learning methods with applications in computer vision, reinforcement learning and artificial intelligence has created a resurgence in the application of these methods to the classification of AD through different imaging modalities. In this study, by utilizing the concept of transfer learning in deep learning, we propose a classification framework to differentiate subjects with Clinical Dementia Rating (CDR) of zero from subjects with CDR greater than zero by using deep learning architectures such as Xception and Inception version 3 in the Keras deep learning library. The attained validation set accuracies are as high as 99.12% for the Inception version 3 network and 97.97% for the Xception network. The presented results suggest that meaningful predictors composed of sMRI and network measures may offer the possibility for early detection of subjects in the early stages of AD.

Includes members:
  Member "visual explanations from deep 3d convolutional neural networks for alzheimer s disease classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding patch based learning by explaining predictions")
  Member "an ensemble of deep convolutional neural networks for alzheimer s disease detection and classification":
    also connected through other hyperedges:
      (none)
  Member "towards alzheimer s disease classification through transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning on biomedical images interactive learning transfer learning class imbalance and beyond")
  Member "xception deep learning with depthwise separable convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation")
  Member "rethinking the inception architecture for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "how transferable are features in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")

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.CV(Computer Vision and Pattern Recognition)