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: "color texture classification based on proposed impulse noise resistant color local binary patterns and significant points selection algorithm"
  Abstract: The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. One of the efficient texture analysis operations is local binary patterns. The proposed approach includes two steps. First, a noise resistant version of color local binary patterns is proposed to decrease sensitivity to noise of LBP. This step is evaluated based on combination of color sensor information using AND operation. In second step, a significant points selection algorithm is proposed to select significant LBP. This phase decreases final computational complexity along with increasing accuracy rate. The Proposed approach is evaluated using Vistex, Outex, and KTH TIPS2a data sets. Our approach has been compared with some state of the art methods. It is experimentally demonstrated that the proposed approach achieves highest accuracy. In two other experiments, result show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi resolution, general usability are other advantages of our proposed approach. In the present paper, a new version of LBP is proposed originally, which is called Hybrid color local binary patterns. It can be used in many image processing applications to extract color and texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.

Connected through hyperedge HE-1 (induced by source paper "texture image analysis and texture classification methods a review"):
  with members:
    - "texture classification approach based on combination of edge a co occurrence and local binary pattern"
    - "color texture classification approach based on combination of primitive pattern units and statistical features"

Connected through hyperedge HE-2 (induced by source paper "a new benchmark dataset for texture image analysis and surface defect detection"):
  with members:
    - "texture image analysis and texture classification methods a review"

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)