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: "residual and plain convolutional neural networks for 3d brain mri classification"
  Abstract: In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, although modern advancements in deep learning for image classification can provide a powerful framework for automatic feature generation and more straightforward analysis. In this paper, we show how similar performance can be achieved skipping these feature extraction steps with the residual and plain 3D convolutional neural network architectures. We demonstrate the performance of the proposed approach for classification of Alzheimer's disease versus mild cognitive impairment and normal controls on the Alzheimer's Disease National Initiative (ADNI) dataset of 3D structural MRI brain scans.

Connected through hyperedge HE-1 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-2 (induced by source paper "neuro dram a 3d recurrent visual attention model for interpretable neuroimaging classification"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "end to end alzheimer s disease diagnosis and biomarker identification"
    - "explaining explanations an overview of interpretability of machine learning"
    - "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-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"
    - "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"
    - "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 "visualizing convolutional networks for mri based diagnosis of alzheimer s disease"):
  with members:
    - "3d cnn based classification using smri and md dti images for alzheimer disease studies"
    - "methods for interpreting and understanding deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "striving for simplicity the all convolutional net"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "3d cnn based classification using smri and md dti images for alzheimer disease studies"):
  with members:
    - "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"
    - "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-6 (induced by source paper "multimodal and multiscale deep neural networks for the early diagnosis of alzheimer s disease using structural mr and fdg pet images"):
  with members:
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

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)