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: "mvdepthnet real time multiview depth estimation neural network"
  Abstract: Although deep neural networks have been widely applied to computer vision problems, extending them into multiview depth estimation is non-trivial. In this paper, we present MVDepthNet, a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints. Multiview observations are encoded in a cost volume and then combined with the reference image to estimate the depth map using an encoder-decoder network. By encoding the information from multiview observations into the cost volume, our method achieves real-time performance and the flexibility of traditional methods that can be applied regardless of the camera intrinsic parameters and the number of images. Geometric data augmentation is used to train MVDepthNet. We further apply MVDepthNet in a monocular dense mapping system that continuously estimates depth maps using a single localized moving camera. Experiments show that our method can generate depth maps efficiently and precisely.

Connected through hyperedge HE-1 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "multi view stereo by temporal nonparametric fusion"
    - "learnable triangulation of human pose"
    - "gradslam dense slam meets automatic differentiation"
    - "superglue learning feature matching with graph neural networks"
    - "efficientdet scalable and efficient object detection"
    - "dpsnet end to end deep plane sweep stereo"

Connected through hyperedge HE-2 (induced by source paper "deepperimeter indoor boundary estimation from posed monocular sequences"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "roomnet end to end room layout estimation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "mvs 2 deep unsupervised multi view stereo with multi view symmetry"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "open world stereo video matching with deep rnn"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-4 (induced by source paper "learning unsupervised multi view stereopsis via robust photometric consistency"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deepmvs learning multi view stereopsis"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "learning a multi view stereo machine"
    - "self supervised learning for stereo matching with self improving ability"
    - "unsupervised learning of depth and ego motion from video"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-5 (induced by source paper "unstructured multi view depth estimation using mask based multiplane representation"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "deeptam deep tracking and mapping"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-6 (induced by source paper "flow motion and depth network for monocular stereo and beyond"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "dpsnet end to end deep plane sweep stereo"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-7 (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"
    - "multi view stereo with single view semantic mesh refinement"
    - "semantically informed multiview surface refinement"
    - "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint"

Connected through hyperedge HE-8 (induced by source paper "multi view stereo by temporal nonparametric fusion"):
  with members:
    - "unstructured multi view depth estimation using mask based multiplane representation"
    - "dpsnet end to end deep plane sweep stereo"
    - "deeptam deep tracking and mapping"
    - "deepmvs learning multi view stereopsis"
    - "gaussian process prior variational autoencoders"
    - "learning a multi view stereo machine"
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"
    - "demon depth and motion network for learning monocular stereo"

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.RO(Robotics)