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: "learned multi patch similarity"
  Abstract: Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine learning, and in particular convolutional neural networks, we propose to learn a matching function which directly maps multiple image patches to a scalar similarity score. Experiments on several multi-view datasets demonstrate that this approach has advantages over methods based on pairwise patch similarity.

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"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi view optimization of local feature geometry"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "optimal multi view correction of local affine frames"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "neighbourhood consensus networks"
    - "megadepth learning single view depth prediction from internet photos"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "mvsnet depth inference for unstructured multi view stereo"

Connected through hyperedge HE-3 (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 mvsnet for high resolution multi view stereo depth inference"
    - "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"

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"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "deeptam deep tracking and mapping"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-5 (induced by source paper "raynet learning volumetric 3d reconstruction with ray potentials"):
  with members:
    - "octnetfusion learning depth fusion from data"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "semantic scene completion from a single depth image"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-6 (induced by source paper "superquadrics revisited learning 3d shape parsing beyond cuboids"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "deepmvs learning multi view stereopsis"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-7 (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"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "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"

Connected through hyperedge HE-8 (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"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "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"

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)