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: "recurrent mvsnet for high resolution multi view stereo depth inference"
  Abstract: Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "multi scale geometric consistency guided multi view stereo"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-2 (induced by source paper "a novel recurrent encoder decoder structure for large scale multi view stereo reconstruction from an open aerial dataset"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "learning a multi view stereo machine"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"

Connected through hyperedge HE-3 (induced by source paper "differentiable volumetric rendering learning implicit 3d representations without 3d supervision"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"

Connected through hyperedge HE-4 (induced by source paper "deep stereo using adaptive thin volume representation with uncertainty awareness"):
  with members:
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "deeptam deep tracking and mapping"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-5 (induced by source paper "normal assisted stereo depth estimation"):
  with members:
    - "self supervised learning for single view depth and surface normal estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "dpsnet end to end deep plane sweep stereo"
    - "ga net guided aggregation net for end to end stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-6 (induced by source paper "cascade cost volume for high resolution multi view stereo and stereo matching"):
  with members:
    - "hierarchical deep stereo matching on high resolution images"
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "group wise correlation stereo network"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deepmvs learning multi view stereopsis"
    - "multi view stereo with asymmetric checkerboard propagation and multi hypothesis joint view selection"
    - "edgestereo a context integrated residual pyramid network for stereo matching"

Connected through hyperedge HE-7 (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"
    - "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"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-8 (induced by source paper "cost volume pyramid based depth inference for multi view stereo"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "learning a multi view stereo machine"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "stereo matching by training a convolutional neural network to compare image patches"

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)