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: "template based monocular 3d shape recovery using laplacian meshes"
  Abstract: We show that by extending the Laplacian formalism, which was first introduced in the Graphics community to regularize 3D meshes, we can turn the monocular 3D shape reconstruction of a deformable surface given correspondences with a reference image into a much better-posed problem. This allows us to quickly and reliably eliminate outliers by simply solving a linear least squares problem. This yields an initial 3D shape estimate, which is not necessarily accurate, but whose 2D projections are. The initial shape is then refined by a constrained optimization problem to output the final surface reconstruction. Our approach allows us to reduce the dimensionality of the surface reconstruction problem without sacrificing accuracy, thus allowing for real-time implementations.

Connected through hyperedge HE-1 (induced by source paper "shape reconstruction by learning differentiable surface representations"):
  with members:
    - "learning elementary structures for 3d shape generation and matching"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "deep learning parametrization for b spline curve approximation"
    - "garnet a two stream network for fast and accurate 3d cloth draping"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "deep geometric prior for surface reconstruction"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "surfnet generating 3d shape surfaces using deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "defslam tracking and mapping of deforming scenes from monocular sequences"):
  with members:
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "learning to reconstruct texture less deformable surfaces from a single view"):
  with members:
    - "geometry aware network for non rigid shape prediction from a single view"
    - "neural face editing with intrinsic image disentangling"
    - "self supervised intrinsic image decomposition"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "learning detailed face reconstruction from a single image"
    - "from shading to local shape"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-4 (induced by source paper "model free consensus maximization for non rigid shapes"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "building statistical shape spaces for 3d human modeling"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "mlpnp a real time maximum likelihood solution to the perspective n point problem"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-5 (induced by source paper "geodesic convolutional shape optimization"):
  with members:
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "toward the coevolution of novel vertical axis wind turbines"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "incremental non rigid structure from motion with unknown focal length"):
  with members:
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "maximizing rigidity revisited a convex programming approach for generic 3d shape reconstruction from multiple perspective views"
    - "volumedeform real time volumetric non rigid reconstruction"

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)