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: "classification of quantitative light induced fluorescence images using convolutional neural network"
  Abstract: Images are an important data source for diagnosis of oral diseases. The manual classification of images may lead to suboptimal treatment procedures due to subjective errors. In this paper an image classification algorithm based on Deep Learning framework is applied to Quantitative Light-induced Fluorescence (QLF) images [4]. The Convolutional Neural Network [3] (CNN) outperforms other state of the art shallow classification models in predicting labels derived from three different dental plaque assessment scores. Such result is possible because our model directly learns invariant feature representations from raw pixel intensity values without any hand-crafted feature engineering. The model benefits from multi-channel representation of the images resulting in improved performance when, besides the Red colour channel, additional Green and Blue colour channels are used. Previous studies on this topic either focused on only single plaque scoring system without providing detailed analysis of results [1] or used a smaller dataset of non-QLF images and a shallow network architecture [2] to address the problem. We expect that Deep Learning of QLF-images can help dental practitioners to perform efficient plaque assessments and contribute to the improvement of patients’ oral health. An extended version of the manuscript with detailed description of the experimental setup and the obtained results can be found at http://arxiv.org/abs/1705.09193 or http://learning-machines.com/.

Connected through hyperedge HE-1 (induced by source paper "automated process incorporating machine learning segmentation and correlation of oral diseases with systemic health"):
  with members:
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "a survey on deep learning in medical image analysis"

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)