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: "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"
  Abstract: Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been focusing on binary classification problems, e.g., distinguishing AD patients from normal control (NC) elderly or mild cognitive impairment (MCI) individuals from NC elderly. However, identifying individuals with AD and MCI, especially MCI individuals who will convert to AD (progressive MCI, pMCI), in a single setting, is needed to achieve the goal of early diagnosis of AD. In this paper, we propose a deep ordinal ranking model for distinguishing NC, stable MCI (sMCI), pMCI, and AD at an individual subject level, taking into account the inherent ordinal severity of brain degeneration caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-category classification problem. The proposed deep ordinal ranking model focuses on the hippocampal morphology of individuals and learns informative and discriminative features automatically. Experiment results based on a large cohort of individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) indicate that the proposed method can achieve better performance than traditional multi-category classification techniques using shape and radiomics features from structural magnetic resonance imaging (MRI) data.

Connected through hyperedge HE-1 (induced by source paper "cumulative link models for deep ordinal classification"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "automatic handgun detection alarm in videos using deep learning"
    - "face detection using deep learning an improved faster rcnn approach"
    - "medical image retrieval using deep convolutional neural network"
    - "deep feature based contextual model for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep ordinal classification based on cumulative link models"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "3d inception based cnn with smri and md dti data fusion for alzheimer s disease diagnostics"):
  with members:
    - "3d cnn based classification using smri and md dti images for alzheimer disease studies"
    - "residual and plain convolutional neural networks for 3d brain mri classification"
    - "a survey on deep learning in medical image analysis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "classification of alzheimer s disease structural mri data by deep learning convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "3d cnn based classification using smri and md dti images for alzheimer disease studies"):
  with members:
    - "residual and plain convolutional neural networks for 3d brain mri classification"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "classification of alzheimer s disease structural mri data by deep learning convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "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-5 (induced by source paper "transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data"):
  with members:
    - "a dynamic graph cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"

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)