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: "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"
  Abstract: This paper intends to lay the theoretical foundation for the method of functional maps, first presented in 2012 by Ovsjanikov, Ben-Chen, Solomon, Butscher and Guibas in the field of the theory and numerics of maps between shapes. We show how to analyze this method by looking at it as an application of the theories of composition operators, of matrix representa- tion of operators on separable Hilbert spaces, and of the theory of the Finite Section Method. These are three well known fruitful topics in functional analysis. When applied to the task of modelling of correspondences of shapes in three-dimensional space, these concepts lead directly to functional maps and its associated functional matrices. Mathematically spoken, functional maps are composition operators between two-dimensional manifolds, and functional matrices are infinite matrix representations of such maps. We present an introduction into the notion and theoretical foundation of the functional analytic framework of the theory of matrix repre- sentation, especially of composition operators. We will also discuss two numerical methods for solving equations with such operators, namely, two variants of the Rectangular Finite Section Method. While one of these, which is well known, leads to an overdetermined system of linear equations, in the second one the minimum-norm solution of an underdetermined system has to be computed. We will present the main convergence results related to these methods.

Connected through hyperedge HE-1 (induced by source paper "functional maps representation on product manifolds"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "localized manifold harmonics for spectral shape analysis"
    - "hamiltonian operator for spectral shape analysis"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "geodesic distance descriptors"
    - "functional correspondence by matrix completion"

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.CG(Computational Geometry)