human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment"):
  Abstract: In this work, we introduce a novel local pairwise descriptor and then develop a simple, effective iterative method to solve the resulting quadratic assignment through sparsity control for shape correspondence between two approximate isometric surfaces. Our pairwise descriptor is based on the stiffness and mass matrix of finite element approximation of the Laplace-Beltrami differential operator, which is local in space, sparse to represent, and extremely easy to compute while containing global information. It allows us to deal with open surfaces, partial matching, and topological perturbations robustly. To solve the resulting quadratic assignment problem efficiently, the two key ideas of our iterative algorithm are: 1) select pairs with good (approximate) correspondence as anchor points, 2) solve a regularized quadratic assignment problem only in the neighborhood of selected anchor points through sparsity control. These two ingredients can improve and increase the number of anchor points quickly while reducing the computation cost in each quadratic assignment iteration significantly. With enough high-quality anchor points, one may use various pointwise global features with reference to these anchor points to further improve the dense shape correspondence. We use various experiments to show the efficiency, quality, and versatility of our method on large data sets, patches, and point clouds (without global meshes).

Includes members:
  Member "self supervised learning of dense shape correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "continuous geodesic convolutions for learning on 3d shapes")
  Member "product manifold filter non rigid shape correspondence via kernel density estimation in the product space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "ds a flexible scalable and provably tight relaxation for matching problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "graph matching relax or not":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised alignment of embeddings with wasserstein procrustes")
  Member "spectral generalized multi dimensional scaling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cyclic functional mapping self supervised correspondence between non isometric deformable shapes")
  Member "discrete laplace beltrami operator determines discrete riemannian metric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parametrizing flat foldable surfaces with incomplete data")

Question: Please tell me which class the source paper 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)