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: "linear shape deformation models with local support using graph based structured matrix factorisation"
  Abstract: Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations.

Connected through hyperedge HE-1 (induced by source paper "fml face model learning from videos"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "learning category specific mesh reconstruction from image collections"
    - "head reconstruction from internet photos"
    - "statistical transformer networks learning shape and appearance models via self supervision"
    - "voxceleb2 deep speaker recognition"
    - "nonlinear 3d face morphable model"
    - "building statistical shape spaces for 3d human modeling"
    - "unrestricted facial geometry reconstruction using image to image translation"

Connected through hyperedge HE-2 (induced by source paper "mesh based autoencoders for localized deformation component analysis"):
  with members:
    - "sparse data driven mesh deformation"
    - "surfnet generating 3d shape surfaces using deep residual networks"
    - "learning shape abstractions by assembling volumetric primitives"
    - "vconv dae deep volumetric shape learning without object labels"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "learning a predictable and generative vector representation for objects"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"

Connected through hyperedge HE-3 (induced by source paper "shape aware surface reconstruction from sparse 3d point clouds"):
  with members:
    - "point set registration coherent point drift"

Connected through hyperedge HE-4 (induced by source paper "shape aware surface reconstruction from sparse data"):
  with members:
    - "point set registration coherent point drift"

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)