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: "simplifying urban data fusion with bigsur"
  Abstract: Our ability to understand data has always lagged behind our ability to collect it. This is particularly true in urban environments, where mass data capture is particularly valuable, but the objects captured are more varied, dense, and complex. Captured data has several problems; it is unstructured (we do not know which objects are encoded by the data), contains noise (the scanning process is often inaccurate) and omissions (it is often impossible to scan all of a building). To understand the structure and content of the environment, we must process the unstructured data to a structured form. #N##N##TAB#BigSURi is an urban reconstruction algorithm which fuses GIS (Geographic Information System / mapping) data, photogrammetric meshes, and street level photography, to create clean representative, semantically labelled, geometry. However, we have identified three problems with the system i) the street level photography is often difficult to acquire; ii) novel facade styles often frustrate the detection of windows and doors; iii) the computational requirements of the system are large, processing a large city block can take up to 15 hours.#N##N##TAB#Here we describe the process of simplifying and validating the BigSUR semantic reconstruction system. In particular, the requirement for street level images is removed, and a greedy post-process profile assignment is introduced to accelerate the system. We accomplish this by modifying the binary integer programming (BIP) optimization, and re-evaluating the effects of various parameters.#N##N##TAB#The new variant of the system is evaluated over a variety of urban areas. We objectively measure mean squared error (MSE) terms over the unstructured geometry, showing that BigSUR is able to accurately recover omissions and discard inaccuracies in the input data. Further, we evaluate the ability of the system to label the walls and roofs of input meshes, concluding that our new BigSUR variant achieves highly accurate semantic labelling with shorter computational time and less input data.

Connected through hyperedge HE-1 (induced by source paper "fast and regularized reconstruction of building facades from street view images using binary integer programming"):
  with members:
    - "yolov3 an incremental improvement"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "pcanet a simple deep learning baseline for image classification"

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)