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: "colorization for image compression"
  Abstract: In this work we focus on the problem of colorization for image compression. Since color information occupies a large proportion of the total storage size of an image, a method that can predict accurate color from its grayscale version can produce dramatic reduction in image file size. But colorization for compression poses several challenges. First, while colorization for artistic purposes simply involves predicting plausible chroma, colorization for compression requires generating output colors that are as close as possible to the ground truth. Second, many objects in the real world exhibit multiple possible colors. Thus, to disambiguate the colorization problem some additional information must be stored to reproduce the true colors with good accuracy. To account for the multimodal color distribution of objects we propose a deep tree-structured network that generates multiple color hypotheses for every pixel from a grayscale picture (as opposed to a single color produced by most prior colorization approaches). We show how to leverage the multimodal output of our model to reproduce with high fidelity the true colors of an image by storing very little additional information. In the experiments we show that our proposed method outperforms traditional JPEG color coding by a large margin, producing colors that are nearly indistinguishable from the ground truth at the storage cost of just a few hundred bytes for high-resolution pictures!

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)