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: "segmentation of natural images by texture and boundary compression"
  Abstract: We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods.

Connected through hyperedge HE-1 (induced by source paper "recent progress in semantic image segmentation"):
  with members:
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "dilated residual networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "real time facial segmentation and performance capture from rgb input"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "model based learning of local image features for unsupervised texture segmentation"):
  with members:
    - "variational multi phase segmentation using high dimensional local features"
    - "a bimodal co sparse analysis model for image processing"
    - "fully convolutional networks for semantic segmentation"
    - "a joint intensity and depth co sparse analysis model for depth map super resolution"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-3 (induced by source paper "photo stylistic brush robust style transfer via superpixel based bipartite graph"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-4 (induced by source paper "co sparse textural similarity for image segmentation"):
  with members:
    - "analysis operator learning and its application to image reconstruction"

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)