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: "fully convolutional densenets for polyp segmentation in colonoscopy"
  Abstract: Early diagnosis and resection of colorectal polyps can effectively reduce the incidence and mortality rate. Colorectal cancer is a common gastrointestinal malignancy, ranking one of the three major malignancies around the world. With the improvement of living standards and dietary habits related problems, the incidence and mortality of colorectal cancer are showing an upward trend. Colorectal cancer is mostly from adenoma polyp malignant change, so early detection has important clinical significance. Although colonoscopy conducted by doctors is considered the most effective way in detecting polyps, uncertainty such as fatigue can affect the results. To solve this problem, we propose a fully convolutional densenet method to achieve the automatic detection and segmentation of colorectal polyps by computer. In this paper, we apply densenet to full convolutional network in segmentation of colorectal polyp, under the condition that not requiring post-processing and pre-training situation, we compare the number of parameters in different layers and assess accuracy and IOU respectively in segmentation of colorectal polyps. The results show that accuracy is improved as the layer increases gradually. When the layer number is 78(N=78), accuracy reaches 97.1% and the average IOU is 83.4%. In addition, we make a comparison with the state-of-the-art polyp segmentation method, the results reveal our method make a considerable improvement.

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.SE(Software Engineering)