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: "large scale visual place recognition with sub linear storage growth"
  Abstract: Robotic and animal mapping systems share many of the same objectives and challenges, but differ in one key aspect: where much of the research in robotic mapping has focused on solving the data association problem, the grid cell neurons underlying maps in the mammalian brain appear to intentionally break data association by encoding many locations with a single grid cell neuron. One potential benefit of this intentional aliasing is both sub-linear map storage and computational requirements growth with environment size, which we demonstrated in a previous proof-of-concept study that detected and encoded mutually complementary co-prime pattern frequencies in the visual map data. In this research, we solve several of the key theoretical and practical limitations of that prototype model and achieve significantly better sub-linear storage growth, a factor reduction in storage requirements per map location, scalability to large datasets on standard compute equipment and improved robustness to environments with visually challenging appearance change. These improvements are achieved through several innovations including a flexible user-driven choice mechanism for the periodic patterns underlying the new encoding method, a parallelized chunking technique that splits the map into sub-sections processed in parallel and a novel feature selection approach that selects only the image information most relevant to the encoded temporal patterns. We evaluate our techniques on two large benchmark datasets with the comparison to the previous state-of-the-art system, as well as providing a detailed analysis of system performance with respect to parameters such as required precision performance and the number of cyclic patterns encoded.

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)