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: "natural boundary conditions for smoothing in geometry processing"
  Abstract: In geometry processing, smoothness energies are commonly used to model scattered data interpolation, dense data denoising, and regularization during shape optimization. The squared Laplacian energy is a popular choice of energy and has a corresponding standard implementation: squaring the discrete Laplacian matrix. For compact domains, when values along the boundary are not known in advance, this construction bakes in low-order boundary conditions. This causes the geometric shape of the boundary to strongly bias the solution. For many applications, this is undesirable. Instead, we propose using the squared Frobenious norm of the Hessian as a smoothness energy. Unlike the squared Laplacian energy, this energy's natural boundary conditions (those that best minimize the energy) correspond to meaningful high-order boundary conditions. These boundary conditions model free boundaries where the shape of the boundary should not bias the solution locally. Our analysis begins in the smooth setting and concludes with discretizations using finite-differences on 2D grids or mixed finite elements for triangle meshes. We demonstrate the core behavior of the squared Hessian as a smoothness energy for various tasks.

Connected through hyperedge HE-1 (induced by source paper "cubic stylization"):
  with members:
    - "semi supervised co analysis of 3d shape styles from projected lines"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "static dynamic filtering for mesh geometry"
    - "thingi10k a dataset of 10 000 3d printing models"

Connected through hyperedge HE-2 (induced by source paper "a smoothness energy without boundary distortion for curved surfaces"):
  with members:
    - "the vector heat method"
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "functional characterization of deformation fields"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-3 (induced by source paper "solid geometry processing on deconstructed domains"):
  with members:
    - "the discretely discontinuous galerkin coarse grid for domain decomposition"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-4 (induced by source paper "efficient inter geodesic distance computation and fast classical scaling"):
  with members:
    - "geodesic distance descriptors"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-5 (induced by source paper "fast classical scaling"):
  with members:
    - "geodesic distance descriptors"
    - "geodesics in heat a new approach to computing distance based on heat flow"

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.GR(Graphics)