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: "gloss color and topography scanning for reproducing a painting s appearance using 3d printing"
  Abstract: High fidelity reproductions of paintings provide new opportunities to museums in preserving and providing access to cultural heritage. This paper presents an integrated system which is able to capture and fabricate color, topography and gloss of a painting, of which gloss capturing forms the most important contribution. A 3D imaging system, using fringe-encoded stereo imaging, is extended to capture spatially-varying gloss, utilizing specular reflectance polarization. The gloss is measured by sampling the specular reflection around Brewster's angle, where these reflections are effectively polarized, and can be separated from the unpolarized, diffuse reflectance. Off-center gloss measurements are calibrated relative to the center measurement. Off-specular gloss measurements, following from local variation of the surface normal, are masked based on the height map and corrected. Shadowed regions, caused by the 3D relief, are treated similarly. The area of a single capture is approximately 180x90mm at a resolution of 25x25micron. Aligned color, height, and gloss tiles are stitched together, registering overlapping color areas. These maps are inputs for a 3D printer. Two paintings were reproduced to verify the effectiveness and efficiency of the proposed system. One painting was scanned four times, consecutively rotated by 90 degrees, to evaluate the influence of the scanning system geometric configuration on the gloss measurement. Experimental results show that the method is sufficiently fast for practical application. The results can well be used for the purpose of physical reproduction and other applications needing first-order estimates of the appearance. Our method to extend appearance scanning with gloss measurements is a valuable addition in the quest for realistic reproductions, in terms of its practical applicability and its perceptual added value, when added to color and topography.

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.GR(Graphics)