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: "towards alzheimer s disease classification through transfer learning"
  Abstract: Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods.

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"
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "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 "a feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirements"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "tensorflow a system for large scale machine learning"
    - "towards open set deep networks"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (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"
    - "identity mappings in deep residual networks"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "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-4 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

Connected through hyperedge HE-5 (induced by source paper "convolutional herbal prescription building method from multi scale facial features"):
  with members:
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "squeeze and excitation networks"
    - "a survey on deep learning in medical image analysis"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "binary classification of alzheimer disease using smri imaging modality and deep learning"):
  with members:
    - "visual explanations from deep 3d convolutional neural networks for alzheimer s disease classification"
    - "an ensemble of deep convolutional neural networks for alzheimer s disease detection and classification"
    - "xception deep learning with depthwise separable convolutions"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural 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.CV(Computer Vision and Pattern Recognition)