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: "two decades of local binary patterns a survey"
  Abstract: Abstract   Texture is an important characteristic for many types of images. In recent years, very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBPs) have been developed, which has led to significant progress in applying texture methods to different problems and applications. Due to this progress, the division between texture descriptors and more generic image or video descriptors has been disappearing. A large number of different variants of LBP have been developed to improve its robustness and to increase its discriminative power and applicability to different types of problems. In this chapter, the most recent and important variants of LBP in 2-D, spatiotemporal, 3-D, and 4-D domains are surveyed. Interesting new developments of LBP in 1-D signal analysis are also considered. Finally, some future challenges for research are presented.

Connected through hyperedge HE-1 (induced by source paper "binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "generative adversarial text to image synthesis"
    - "convolutional two stream network fusion for video action recognition"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "deep filter banks for texture recognition description and segmentation"
    - "p cnn pose based cnn features for action recognition"
    - "deep residual learning for image 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)