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: "robust and high fidelity mesh denoising"
  Abstract: This paper presents a simple and effective two-stage mesh denoising algorithm, where in the first stage, the face normal filtering is done by using the bilateral normal filtering in the robust statistics framework. Tukey's bi-weight function is used as similarity function in the bilateral weighting, which is a robust estimator and stops the diffusion at sharp edges to retain features and removes noise from flat regions effectively. In the second stage, an edge weighted Laplace operator is introduced to compute a differential coordinate. This differential coordinate helps the algorithm to produce a high-quality mesh without any face normal flips and makes the method robust against high-intensity noise.

Connected through hyperedge HE-1 (induced by source paper "an end to end geometric deficiency elimination algorithm for 3d meshes"):
  with members:
    - "incremental visual inertial 3d mesh generation with structural regularities"
    - "l3doc lifelong 3d object classification"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "shapenet an information rich 3d model repository"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-2 (induced by source paper "hlo half kernel laplacian operator for surface smoothing"):
  with members:
    - "mesh denoising based on normal voting tensor and binary optimization"
    - "low rank matrix approximation for geometry filtering"

Connected through hyperedge HE-3 (induced by source paper "normalnet learning based guided normal filtering for mesh denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "mesh based autoencoders for localized deformation component analysis"

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)