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: "semantically informed multiview surface refinement"
  Abstract: We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency and the compatibility of the semantic segmentations across a set of calibrated images. Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D. In the semantic segmentation step, the labels on the mesh are updated with MRF inference, such that they are compatible with the semantic segmentations in the input images. Also, this step includes prior assumptions about the surface shape of different semantic classes. The priors induce a tight coupling, where semantic information influences the shape update and vice versa. Specifically, we introduce priors that favor (i) adaptive smoothing, depending on the class label; (ii) straightness of class boundaries; and (iii) semantic labels that are consistent with the surface orientation. The novel mesh-based reconstruction is evaluated in a series of experiments with real and synthetic data. We compare both to state-of-the-art, voxel-based semantic 3D reconstruction, and to purely geometric mesh refinement, and demonstrate that the proposed scheme yields improved 3D geometry as well as an improved semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "resdepth learned residual stereo reconstruction"):
  with members:
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "dsm building shape refinement from combined remote sensing images based on wnet cgans"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "deepmvs learning multi view stereopsis"
    - "pyramid stereo matching network"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-2 (induced by source paper "tapa mvs textureless aware patchmatch multi view stereo"):
  with members:
    - "mesh based camera pairs selection and occlusion aware masking for mesh refinement"
    - "a data driven prior on facet orientation for semantic mesh labeling"
    - "deepmvs learning multi view stereopsis"
    - "multi view stereo with asymmetric checkerboard propagation and multi hypothesis joint view selection"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "multi view stereo with single view semantic mesh refinement"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"

Connected through hyperedge HE-3 (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"
    - "mesh based 3d textured urban mapping"
    - "detail preserving and content aware variational multi view stereo reconstruction"

Connected through hyperedge HE-4 (induced by source paper "a data driven prior on facet orientation for semantic mesh labeling"):
  with members:
    - "multi view stereo with single view semantic mesh refinement"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "efficient 2d and 3d facade segmentation using auto context"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"
    - "reseg a recurrent neural network based model for semantic segmentation"

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)