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: "discrete laplace beltrami operator determines discrete riemannian metric"
  Abstract: The Laplace-Beltrami operator of a smooth Riemannian manifold is determined by the Riemannian metric. Conversely, the heat kernel constructed from its eigenvalues and eigenfunctions determines the Riemannian metric. This work proves the analogy on Euclidean polyhedral surfaces (triangle meshes), that the discrete Laplace-Beltrami operator and the discrete Riemannian metric (unique up to a scaling) are mutually determined by each other. Given an Euclidean polyhedral surface, its Riemannian metric is represented as edge lengths, satisfying triangle inequalities on all faces. The Laplace-Beltrami operator is formulated using the cotangent formula, where the edge weight is defined as the sum of the cotangent of angles against the edge. We prove that the edge lengths can be determined by the edge weights unique up to a scaling using the variational approach. First, we show that the space of all possible metrics of a polyhedral surface is convex. Then, we construct a special energy defined on the metric space, such that the gradient of the energy equals to the edge weights. Third, we show the Hessian matrix of the energy is positive definite, restricted on the tangent space of the metric space, therefore the energy is convex. Finally, by the fact that the parameter on a convex domain and the gradient of a convex function defined on the domain have one-to-one correspondence, we show the edge weights determines the polyhedral metric unique up to a scaling. The constructive proof leads to a computational algorithm that finds the unique metric on a topological triangle mesh from a discrete Laplace-Beltrami operator matrix.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment"):
  with members:
    - "self supervised learning of dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "graph matching relax or not"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-2 (induced by source paper "parametrizing flat foldable surfaces with incomplete data"):
  with members:
    - "trim triangulating images for efficient registration"

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.DM(Discrete Mathematics)