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: "density adaptive kernel based re ranking for person re identification"
  Abstract: Person Re-Identification (ReID) refers to the task of verifying the identity of a pedestrian observed from non-overlapping views of surveillance cameras networks. Recently, it has been validated that re-ranking could bring remarkable performance improvements for a person ReID system. However, the current re-ranking approaches either require feedbacks from users or suffer from burdensome computation cost. In this paper, we propose to exploit a density-adaptive smooth kernel technique to perform efficient and effective re-ranking. Specifically, we adopt a smooth kernel function to formulate the neighboring relationship amongst data samples with a density-adaptive parameter. Based on the new formulation, we present two simple yet effective re-ranking methods, termed inverse Density-Adaptive Kernel based Re-ranking (inv-DAKR) and bidirectional Density-Adaptive Kernel based Re-ranking (bi-DAKR), in which the local density information around each gallery sample is elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR to incorporate the available extra probe samples and demonstrate that the extra probe samples are able to improve the local neighborhood and thus further refine the ranking result. Extensive experiments are conducted on six benchmark datasets, including PRID450s, VIPeR, CUHK03, GRID, Market-1501 and Mars. Experimental results demonstrate that our proposals are effective and efficient.

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)