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: "functional correspondence by matrix completion"
  Abstract: In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional correspondence problem as matrix completion with manifold geometric structure and inducing functional localization with the $L_1$ norm. We discuss efficient numerical procedures for the solution of our problem. Our method compares favorably to the accuracy of state-of-the-art correspondence algorithms on non-rigid shape matching benchmarks, and is especially advantageous in settings when only scarce data is available.

Connected through hyperedge HE-1 (induced by source paper "deep geometric matrix completion are we doing it right"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "simplifying graph convolutional networks"
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "neural network matrix factorization"
    - "matrix completion on graphs"

Connected through hyperedge HE-2 (induced by source paper "shape analysis via functional map construction and bases pursuit"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "robust structure based shape correspondence"
    - "point wise map recovery and refinement from functional correspondence"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep learning for structured shape matching"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "partial functional correspondence"

Connected through hyperedge HE-4 (induced by source paper "functional maps representation on product manifolds"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "reversible harmonic maps between discrete surfaces"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "efficient deformable shape correspondence via kernel matching"
    - "point wise map recovery and refinement from functional correspondence"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-6 (induced by source paper "deep functional maps structured prediction for dense shape correspondence"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "dense human body correspondences using convolutional networks"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-7 (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"):
  with members:
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "spectrometer amortized sublinear spectral approximation of distance on graphs"
    - "point wise map recovery and refinement from functional correspondence"
    - "on the optimality of shape and data representation in the spectral domain"
    - "matching lbo eigenspace of non rigid shapes via high order statistics"
    - "spectral generalized multi dimensional scaling"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "efficient deformable shape correspondence via kernel matching"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geodesic distance descriptors"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "spectral descriptors for graph matching"
    - "spectral generalized multi dimensional scaling"
    - "coupled quasi harmonic bases"

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)