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: "semi global weighted least squares in image filtering"
  Abstract: Solving the global method of Weighted Least Squares (WLS) model in image filtering is both time- and memory-consuming. In this paper, we present an alternative approximation in a time- and memory- efficient manner which is denoted as Semi-Global Weighed Least Squares (SG-WLS). Instead of solving a large linear system, we propose to iteratively solve a sequence of subsystems which are one-dimensional WLS models. Although each subsystem is one-dimensional, it can take two-dimensional neighborhood information into account due to the proposed special neighborhood construction. We show such a desirable property makes our SG-WLS achieve close performance to the original two-dimensional WLS model but with much less time and memory cost. While previous related methods mainly focus on the 4-connected/8-connected neighborhood system, our SG-WLS can handle a more general and larger neighborhood system thanks to the proposed fast solution. We show such a generalization can achieve better performance than the 4-connected/8-connected neighborhood system in some applications. Our SG-WLS is $\sim20$ times faster than the WLS model. For an image of $M\times N$, the memory cost of SG-WLS is at most at the magnitude of $max\{\frac{1}{M}, \frac{1}{N}\}$ of that of the WLS model. We show the effectiveness and efficiency of our SG-WLS in a range of applications.

Connected through hyperedge HE-1 (induced by source paper "real time image smoothing via iterative least squares"):
  with members:
    - "quantitative evaluation of base and detail decomposition filters based on their artifacts"
    - "deep bilateral learning for real time image enhancement"
    - "image to image translation with conditional adversarial networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "image smoothing via unsupervised learning"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "deep image prior"
    - "deep texture and structure aware filtering network for image smoothing"
    - "deep bilateral learning for real time image enhancement"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "fast image processing with fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "the domain transform solver"):
  with members:
    - "fast end to end trainable guided filter"
    - "learning affinity via spatial propagation networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "semantic image segmentation with task specific edge detection using cnns and a discriminatively trained domain transform"
    - "the fast bilateral solver"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-4 (induced by source paper "deep texture and structure aware filtering network for image smoothing"):
  with members:
    - "co occurrence filter"
    - "convolutional neural pyramid for image processing"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "fast image processing with fully convolutional networks"
    - "dehazenet an end to end system for single image haze removal"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "holistically nested edge detection"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)