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: "accurate brain extraction using active shape model and convolutional neural networks"
  Abstract: Brain extraction or skull stripping is a fundamental procedure in most of neuroimaging processing systems. The performance of this procedure has had a critical impact on the success of neuroimaging analysis. After several years of research and development, brain extraction still remains a challenging problem. In this paper, we propose an effective method for skull stripping in Magnetic Resonance Imaging (MRI) scans named ASM-CNN. Our system is a combination of Active Shape Model (ASM) and Convolutional Neural Network (CNN), taking full advantage of these two methods to achieve remarkable results. Instead of working with 3D structures, we process 2D image sequences in sagittal plane. First, we divide images into different groups such that, in each group, the shapes and structures of brain boundaries have similar appearances. This allows developing precise algorithms for each group in order to produce high performance segmentation results. Second, a modified version of ASM is used to detect the brain boundary in images by utilizing prior knowledge of each group. Finally, CNN and the post-processing methods such as Conditional Random Field, Gaussian Process and some special rules are applied to refine segmentation contour produced by ASM. We compared ASM-CNN with the latest version of five state-of-the-art, publicly available methods, namely BET, BSE, 3DSS, ROBEX and BEAST. The evaluation was carried out by using three public datasets IBSR, LPBA and OASIS. The experimental results show that the proposed method outperforms five states-of-the-art algorithms, surpassing all the other methods by a significant margin in all experiments.

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)