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: "end to end alzheimer s disease diagnosis and biomarker identification"
  Abstract: As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer’s disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.

Connected through hyperedge HE-1 (induced by source paper "representation learning with statistical independence to mitigate bias"):
  with members:
    - "repair removing representation bias by dataset resampling"
    - "adversarial invariant feature learning with accuracy constraint for domain generalization"
    - "mimetics towards understanding human actions out of context"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "on the global optima of kernelized adversarial representation learning"
    - "why can t i dance in the mall learning to mitigate scene bias in action recognition"
    - "learning not to learn training deep neural networks with biased data"
    - "mitigating unwanted biases with adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "neural abstractive text summarization and fake news detection"):
  with members:
    - "attending to entities for better text understanding"
    - "abstractive text summarization by incorporating reader comments"
    - "abstractive and extractive text summarization using document context vector and recurrent neural networks"
    - "end to end parkinson disease diagnosis using brain mr images by 3d cnn"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "wikihow a large scale text summarization dataset"
    - "text summarization techniques a brief survey"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "neuro dram a 3d recurrent visual attention model for interpretable neuroimaging classification"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "explaining explanations an overview of interpretability of machine learning"
    - "residual and plain convolutional neural networks for 3d brain mri classification"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "confounder aware visualization of convnets"):
  with members:
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-5 (induced by source paper "forecasting the progression of alzheimer s disease using neural networks and a novel pre processing algorithm"):
  with members:
    - "robust training of recurrent neural networks to handle missing data for disease progression modeling"

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)