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: "anisotropic mesh adaptation for image representation and scaling"
  Abstract: Triangular meshes have gained much interest in image representation and have been widely used in image processing. This paper introduces a particular anisotropic mesh adaptation (AMA) method to image representation and image scaling. The AMA method is based on metric-specified mesh adaptation and finite element interpolation for Delaunay triangles. An initial triangular mesh is generated based on the desired number of points, and the mesh is then adapted according to a metric tensor that controls the size, shape and orientation of the triangles. Finally, the image is reconstructed from the mesh using finite element interpolation. The method is denoted as AMA-L if linear interpolation is used and AMA-Q if quadratic interpolation is used in the reconstruction, respectively. Different than many other methods, the AMA method starts with a triangular mesh directly and then adapts the mesh to represent the image. This AMA method has clear mathematical framework and can improve computational efficiency and accuracy in image processing. The AMA representation method provides comparable results with other content-based adaptive schemes but requires lower computational cost, and the AMA scaling method uses finite element interpolation and is comparable to vectorization.

Connected through hyperedge HE-1 (induced by source paper "optimising spatial and tonal data for pde based inpainting"):
  with members:
    - "a bi level view of inpainting based image compression"
    - "from bits to images inversion of local binary descriptors"

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)