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: "shape aware surface reconstruction from sparse data"
  Abstract: The  reconstruction  of  an  object's  shape  or  surface  from  a  set  of  3D  points  is  a  common  topic  in  materials  and  life sciences, computationally handled in computer graphics.  Such points usually stem from optical or tactile 3D coordinate measuring equipment.  Surface reconstruction also appears in medical image analysis, e.g.  in anatomy reconstruction from  tomographic  measurements  or  the  alignment  of  intra-operative  navigation  and  preoperative  planning  data.   In contrast to mere 3D point clouds, medical imaging yields contextual information on the 3D point data that can be used to adopt prior information on the shape that is to be reconstructed from the measurements.  In this work we propose to use a statistical shape model (SSM) as a prior for surface reconstruction.  The prior knowledge is represented by a point distribution model (PDM) that is associated with a surface mesh.  Using the shape distribution that is modelled by the PDM, we reformulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given measurements are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are oriented according to the surface normals at the PDM points, a surface-based  tting is accomplished.  By estimating the parameters of the GMM in a maximum a posteriori manner, the  reconstruction  of  the  surface  from  the  given  measurements  is  achieved.   Extensive  experiments  suggest  that  our proposed approach leads to superior surface reconstructions compared to Iterative Closest Point (ICP) methods.

Connected through hyperedge HE-1 (induced by source paper "linear shape deformation models with local support using graph based structured matrix factorisation"):
  with members:
    - "robust principal component analysis on graphs"
    - "functional correspondence by matrix completion"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
    - "robust principal component analysis"
    - "convex sparse matrix factorizations"

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)