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: "texture characterization using shape co occurrence patterns"
  Abstract: Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occurrence patterns. More precisely, texture images are first represented by a tree of shapes, each of which is associated with several geometrical and radiometric attributes. Then, four typical kinds of shape co-occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords. Three different coding methods are investigated for learning the codewords, which can be used to encode any given texture image into a descriptive vector. In contrast with existing works, the proposed approach not only inherits the shape-based method’s strong ability to capture geometrical aspects of textures and high robustness to variations in imaging conditions but also provides a flexible way to consider shape relationships and to compute high-order statistics on the tree. To the best of our knowledge, this is the first time that co-occurrence patterns of explicit shapes have been used as a tool for texture analysis. Experiments on various texture and scene data sets demonstrate the efficiency of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-2 (induced by source paper "conditional generative convnets for exemplar based texture synthesis"):
  with members:
    - "learning the synthesizability of dynamic texture samples"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "spectral normalization for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "a survey of exemplar based texture synthesis"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "improved training of wasserstein gans"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-3 (induced by source paper "learning the synthesizability of dynamic texture samples"):
  with members:
    - "deep visual attention prediction"
    - "video salient object detection via fully convolutional networks"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "texture synthesis using convolutional neural networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "describing textures in the wild"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-4 (induced by source paper "learning transferable deep models for land use classification with high resolution remote sensing images"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "dota a large scale dataset for object detection in aerial images"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "exploiting deep features for remote sensing image retrieval a systematic investigation"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "distributed mining of large scale remote sensing image archives on public computing infrastructures"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)