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: "neonatal pain expression recognition using transfer learning"
  Abstract: Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has been successfully applied to images for different classification tasks. In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning. Specifically, we propose to exploit a pre-trained CNN that was originally trained on a relatively similar dataset for face recognition (VGG Face) as well as CNNs that were pre-trained on a relatively different dataset for image classification (iVGG F,M, and S) to extract deep features from neonates' faces. In the final stage, several supervised machine learning classifiers are trained to classify neonates' facial expression into pain or no pain expression. The proposed pipeline achieved, on a testing dataset, 0.841 AUC and 90.34 accuracy, which is approx. 7 higher than the accuracy of handcrafted traditional features. We also propose to combine deep features with traditional features and hypothesize that the mixed features would improve pain classification performance. Combining deep features with traditional features achieved 92.71 accuracy and 0.948 AUC. These results show that transfer learning, which is a faster and more practical option than training CNN from the scratch, can be used to extract useful features for pain expression recognition in neonates. It also shows that combining deep features with traditional handcrafted features is a good practice to improve the performance of pain expression recognition and possibly the performance of similar applications.

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)