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: "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"
  Abstract: We propose an approach for dense semantic 3D reconstruction which uses a data term that is defined as potentials over viewing rays, combined with continuous surface area penalization. Our formulation is a convex relaxation which we augment with a crucial non-convex constraint that ensures exact handling of visibility. To tackle the non-convex minimization problem, we propose a majorize-minimize type strategy which converges to a critical point. We demonstrate the benefits of using the non-convex constraint experimentally. For the geometry-only case, we set a new state of the art on two datasets of the commonly used Middlebury multi-view stereo benchmark. Moreover, our general-purpose formulation directly reconstructs thin objects, which are usually treated with specialized algorithms. A qualitative evaluation on the dense semantic 3D reconstruction task shows that we improve significantly over previous methods.

Connected through hyperedge HE-1 (induced by source paper "routedfusion learning real time depth map fusion"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "surfelmeshing online surfel based mesh reconstruction"
    - "mesh r cnn"
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-2 (induced by source paper "a cross season correspondence dataset for robust semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "vlase vehicle localization by aggregating semantic edges"
    - "long term visual localization using semantically segmented images"
    - "image to image translation for domain adaptation"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-3 (induced by source paper "semi supervised semantic mapping through label propagation with semantic texture meshes"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "splatnet sparse lattice networks for point cloud processing"
    - "tangent convolutions for dense prediction in 3d"
    - "semantic visual localization"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "neural volumes learning dynamic renderable volumes from images"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "video to video synthesis"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deep appearance models for face rendering"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-5 (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"
    - "semantically informed multiview surface refinement"

Connected through hyperedge HE-6 (induced by source paper "dense 3d visual mapping via semantic simplification"):
  with members:
    - "incremental reconstruction of urban environments by edge points delaunay triangulation"

Connected through hyperedge HE-7 (induced by source paper "3d shape reconstruction from a single 2d image via 2d 3d self consistency"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "deep single view 3d object reconstruction with visual hull embedding"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "weakly supervised 3d reconstruction with adversarial constraint"

Connected through hyperedge HE-8 (induced by source paper "multi view consistency as supervisory signal for learning shape and pose prediction"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "weakly supervised 3d reconstruction with adversarial constraint"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "learning a predictable and generative vector representation for objects"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"

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)