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: "user assisted and automatic inverse procedural road modelling at the city scale"
  Abstract: Cities are structured by roads. Having up to date and detailed maps of these is thus an important challenge for urban planing, civil engineering and transportation. Those maps are traditionally created manually, which represents a massive amount of work, and may discard recent or temporary changes. For these reasons, automated map building has been a long time goal, either for road network reconstruction or for local road surface reconstruction from low level observations. In this work, we navigate between these two goals. Starting from an approximate road axis (+ width) network as a simple road modelling, we propose to use observations of street features and optimisation to improve the coarse model. Observations are generic, and as such, can be derived from various data, such as aerial images, street images and street Lidar, other GIS data, and complementary user input. #R##N#Starting from an initial road modelling which is at a median distance of 1.5 metre from the sidewalk ground-truth, our method has the potential to robustly optimise the road modelling so the median distance reaches 0.45 metre fully automatically, with better results possible using user inputs and/or more precise observations. The robust non linear least square optimisation used is extremely fast, with computing time from few minutes (whole Paris) to less than a second for a few blocks. #R##N#The proposed approach is simple, very fast and produces a credible road model. These promising results open the way to various applications, such as integration in an interactive framework, or integration in a more powerful optimisation method, which would be able to further segment road network and use more complex road model.

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.CE(Computational Engineering, Finance, and Science)