human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "continuous 3d label stereo matching using local expansion moves":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep depth prior for multi view stereo")
  Member "look wider to match image patches with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end learning of multi scale convolutional neural network for stereo matching")
  Member "cascade residual learning a two stage convolutional neural network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep depth prior for multi view stereo")
  Member "detect replace refine deep structured prediction for pixel wise labeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resdepth learned residual stereo reconstruction")
  Member "improved stereo matching with constant highway networks and reflective confidence learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty estimation for end to end learned dense stereo matching via probabilistic deep learning")
  Member "end to end training of hybrid cnn crf models for stereo":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "computing the stereo matching cost with a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation")
  Member "stereo matching by training a convolutional neural network to compare image patches":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")

Question: Please tell me which class the source paper 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)