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: "coronary artery segmentation from intravascular optical coherence tomography using deep capsules"
  Abstract: The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. However, automated, robust IVOCT image analysis tools are lacking. Current image processing methods are hindered by the time needed to generate these expert-labelled datasets and also the potential for bias during the analysis. Here we present a new deep learning method based on capsules to automatically produce lumen segmentations, built using a large IVOCT dataset of 12,011 images with ground-truth segmentations. This dataset contains images with both blood and light artefacts (22.8%), as well as noise from metallic (23.1%) and bioresorbable stents (2.5%). We trained our model on a dataset containing 9,608 images. We rigorously investigate design variations with respect to upsampling regimes and input selection and validate our deep learning model using 2,403 images. We show that our fully trained and optimized model achieves a mean Soft Dice Score of 97.11% (median of 98.2%), segments 200 IVOCT images in an acceptable timeframe of 12 seconds and outperforms current algorithms.

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.LG(Machine Learning)