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: "estimation of tissue oxygen saturation from rgb images and sparse hyperspectral signals based on conditional generative adversarial network"
  Abstract: Intra-operative measurement of tissue oxygen saturation (#N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                  ) is important in detection of ischaemia, monitoring perfusion and identifying disease. Hyperspectral imaging (HSI) measures the optical reflectance spectrum of the tissue and uses this information to quantify its composition, including #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                  . However, real-time monitoring is difficult due to capture rate and data processing time. An endoscopic system based on a multi-fibre probe was previously developed to sparsely capture HSI data (sHSI). These were combined with RGB images, via a deep neural network, to generate high-resolution hypercubes and calculate #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                  . To improve accuracy and processing speed, we propose a dual-input conditional generative adversarial network, Dual2StO2, to directly estimate #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                   by fusing features from both RGB and sHSI. Validation experiments were carried out on in vivo porcine bowel data, where the ground truth #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                   was generated from the HSI camera. Performance was also compared to our previous super-spectral-resolution network, SSRNet in terms of mean #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                   prediction accuracy and structural similarity metrics. Dual2StO2 was also tested using simulated probe data with varying fibre number. #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                   estimation by Dual2StO2 is visually closer to ground truth in general structure and achieves higher prediction accuracy and faster processing speed than SSRNet. Simulations showed that results improved when a greater number of fibres are used in the probe. Future work will include refinement of the network architecture, hardware optimization based on simulation results, and evaluation of the technique in clinical applications beyond #N#                    #N#                      #N#                    #N#                    $${\hbox {StO}}_2$$#N#                    #N#                      #N#                    #N#                   estimation.

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)