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: "cbmv a coalesced bidirectional matching volume for disparity estimation"
  Abstract: Recently, there has been a paradigm shift in stereo matching with learning-based methods achieving the best results on all popular benchmarks. The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions. Many of these assumptions, however, had been validated extensively and hold for the majority of possible inputs. In this paper, we generate a matching volume leveraging both data with ground truth and conventional wisdom. We accomplish this by coalescing diverse evidence from a bidirectional matching process via random forest classifiers. We show that the resulting matching volume estimation method achieves similar accuracy to purely data-driven alternatives on benchmarks and that it generalizes to unseen data much better. In fact, the results we submitted to the KITTI and ETH3D benchmarks were generated using a classifier trained on the Middlebury 2014 dataset.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation for depth prediction from images"):
  with members:
    - "learning depth with convolutional spatial propagation network"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "continuous 3d label stereo matching using local expansion moves"

Connected through hyperedge HE-2 (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-3 (induced by source paper "group wise correlation stereo network"):
  with members:
    - "feature intertwiner for object detection"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "group normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "open world stereo video matching with deep rnn"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

Connected through hyperedge HE-4 (induced by source paper "hierarchical deep stereo matching on high resolution images"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "continuous 3d label stereo matching using local expansion moves"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-5 (induced by source paper "cnn based cost volume analysis as confidence measure for dense matching"):
  with members:
    - "octnet learning deep 3d representations at high resolutions"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "fpnn field probing neural networks for 3d data"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "real time self adaptive deep stereo"):
  with members:
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "guided stereo matching"
    - "group wise correlation stereo network"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"

Connected through hyperedge HE-7 (induced by source paper "semi dense stereo matching using dual cnns"):
  with members:
    - "open world stereo video matching with deep rnn"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "intel realsense stereoscopic depth cameras"
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"

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)