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: "detail preserving and content aware variational multi view stereo reconstruction"
  Abstract: Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view images is a fundamental yet active research area in computer vision. Despite the steady progress in multi-view stereo (MVS) reconstruction, many existing methods are still limited in recovering fine-scale details and sharp features while suppressing noises, and may fail in reconstructing regions with less textures. To address these limitations, this paper presents a detail-preserving and content-aware variational (DCV) MVS method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising. In reprojection error minimization, we propose a novel inter-image similarity measure, which is effective to preserve fine-scale details of the reconstructed surface and builds a connection between guided image filtering and image registration. In mesh denoising, we propose a content-aware    $\ell _{p}$   -minimization algorithm by adaptively estimating the    $p$    value and regularization parameters. Compared with conventional isotropic mesh smoothing approaches, the proposed method is much more promising in suppressing noise while preserving sharp features. Experimental results on benchmark data sets demonstrate that our DCV method is capable of recovering more surface details, and obtains cleaner and more accurate reconstructions than the state-of-the-art methods. In particular, our method achieves the best results among all published methods on the Middlebury dino ring and dino sparse data sets in terms of both completeness and accuracy.

Connected through hyperedge HE-1 (induced by source paper "mesh based camera pairs selection and occlusion aware masking for mesh refinement"):
  with members:
    - "tapa mvs textureless aware patchmatch multi view stereo"
    - "predicting the next best view for 3d mesh refinement"
    - "multi view stereo with single view semantic mesh refinement"
    - "semantically informed multiview surface refinement"
    - "mesh based 3d textured urban mapping"

Connected through hyperedge HE-2 (induced by source paper "multi view stereo 3d edge reconstruction"):
  with members:
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"

Connected through hyperedge HE-3 (induced by source paper "mesh based 3d textured urban mapping"):
  with members:
    - "automatic 3d reconstruction of manifold meshes via delaunay triangulation and mesh sweeping"
    - "robust moving objects detection in lidar data exploiting visual cues"
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"
    - "efficient moving point handling for incremental 3d manifold reconstruction"

Connected through hyperedge HE-4 (induced by source paper "semantically informed multiview surface refinement"):
  with members:
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"

Connected through hyperedge HE-5 (induced by source paper "automatic 3d reconstruction of manifold meshes via delaunay triangulation and mesh sweeping"):
  with members:
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"
    - "efficient moving point handling for incremental 3d manifold 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)