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: "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
  Abstract: Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US. Neurocognitive tests have been used to both assess the patient condition and to monitor the patient progress. This work aims to directly use diffusion MR images taken shortly after injury to detect whether a patient suffers from mTBI, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images. to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.

Connected through hyperedge HE-1 (induced by source paper "deepiris iris recognition using a deep learning approach"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "fingernet pushing the limits of fingerprint recognition using convolutional neural network"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "mtbi identification from diffusion mr images using bag of adversarial visual features"):
  with members:
    - "identifying mild traumatic brain injury patients from mr images using bag of visual words"
    - "a machine learning approach for identifying patients with mild traumatic brain injury using diffusion mri modeling"
    - "image captioning with semantic attention"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)