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: "de noising stabilizing and completing 3d reconstructions on the go using plane priors"
  Abstract: Creating 3D maps on robots and other mobile devices has become a reality in recent years. Online 3D reconstruction enables many exciting applications in robotics and AR/VR gaming. However, the reconstructions are noisy and generally incomplete. Moreover, during onine reconstruction, the surface changes with every newly integrated depth image which poses a significant challenge for physics engines and path planning algorithms. This paper presents a novel, fast and robust method for obtaining and using information about planar surfaces, such as walls, floors, and ceilings as a stage in 3D reconstruction based on Signed Distance Fields. Our algorithm recovers clean and accurate surfaces, reduces the movement of individual mesh vertices caused by noise during online reconstruction and fills in the occluded and unobserved regions. We implemented and evaluated two different strategies to generate plane candidates and two strategies for merging them. Our implementation is optimized to run in real-time on mobile devices such as the Tango tablet. In an extensive set of experiments, we validated that our approach works well in a large number of natural environments despite the presence of significant amount of occlusion, clutter and noise, which occur frequently. We further show that plane fitting enables in many cases a meaningful semantic segmentation of real-world scenes.

Connected through hyperedge HE-1 (induced by source paper "efficient plane based optimization of geometry and texture for indoor rgb d reconstruction"):
  with members:
    - "plane based optimization of geometry and texture for rgb d reconstruction of indoor scenes"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "fine to coarse global registration of rgb d scans"
    - "surface approximation via asymptotic optimal geometric partition"

Connected through hyperedge HE-2 (induced by source paper "oriented point sampling for plane detection in unorganized point clouds"):
  with members:
    - "pop up slam semantic monocular plane slam for low texture environments"
    - "plane extraction from depth data using a gaussian mixture regression model"

Connected through hyperedge HE-3 (induced by source paper "indoor geonet weakly supervised hybrid learning for depth and pose estimation"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "geometry aware deep network for single image novel view synthesis"
    - "unsupervised learning of depth and ego motion from video"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "sfm net learning of structure and motion from video"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"

Connected through hyperedge HE-4 (induced by source paper "primitive fitting using deep boundary aware geometric segmentation"):
  with members:
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "multi class model fitting by energy minimization and mode seeking"
    - "casenet deep category aware semantic edge detection"
    - "real time 3d scene description using spheres cones and cylinders"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "plane based optimization of geometry and texture for rgb d reconstruction of indoor scenes"):
  with members:
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "fine to coarse global registration of rgb d scans"
    - "surface approximation via asymptotic optimal geometric partition"

Connected through hyperedge HE-6 (induced by source paper "psdf fusion probabilistic signed distance function for on the fly 3d data fusion and scene reconstruction"):
  with members:
    - "an efficient volumetric mesh representation for real time scene reconstruction using spatial hashing"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-7 (induced by source paper "fine to coarse global registration of rgb d scans"):
  with members:
    - "3dmatch learning local geometric descriptors from rgb d reconstructions"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "scenenet understanding real world indoor scenes with synthetic data"
    - "convolutional neural network based place recognition"
    - "contextually guided semantic labeling and search for 3d point clouds"

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)