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: "shape recognition by combining contour and skeleton into a mid level representation"
  Abstract: Contour and skeleton are two main stream representations for shape recognition in the literature. It has been shown that such two representations convey complementary information, however combining them in a nature way is nontrivial, as they are generally abstracted by different structures (closed string vs graph), respectively. This paper aims at addressing the shape recognition problem by combining contour and skeleton into a mid-level of shape representation. To form a mid-level representation for shape contours, a recent work named Bag of Contour Fragments (BCF) is adopted; While for skeleton, a new mid-level representation named Bag of Skeleton Paths (BSP) is proposed, which is formed by pooling the skeleton codes by encoding the skeleton paths connecting pairs of end points in the skeleton. Finally, a compact shape feature vector is formed by concatenating BCF with BSP and fed into a linear SVM classifier to recognize the shape. Although such a concatenation is simple, the SVM classifier can automatically learn the weights of contour and skeleton features to offer discriminative power. The encouraging experimental results demonstrate that the proposed new shape representation is effective for shape classification and achieves the state-of-the-art performances on several standard shape benchmarks.

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)