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 "end to end training of hybrid cnn crf models for stereo"):
  Abstract: We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply a recently proposed highly parallel dual block descent algorithm which only needs a small fixed number of iterations to compute a high-quality approximate minimizer. As the main contribution of the paper, we propose a theoretically sound method based on the structured output support vector machine (SSVM) to train the hybrid CNN+CRF model on large-scale data end-to-end. Our trained models perform very well despite the fact that we are using shallow CNNs and do not apply any kind of post-processing to the final output of the CRF. We evaluate our combined models on challenging stereo benchmarks such as Middlebury 2014 and Kitti 2015 and also investigate the performance of each individual component.

Includes members:
  Member "sublabel accurate convex relaxation of vectorial multilabel energies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lifting vectorial variational problems a natural formulation based on geometric measure theory and discrete exterior calculus")
  Member "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "complexity of discrete energy minimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lifting vectorial variational problems a natural formulation based on geometric measure theory and discrete exterior calculus")
  Member "solving dense image matching in real time using discrete continuous optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learned collaborative stereo refinement")
  Member "semantic image segmentation via deep parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "computing the stereo matching cost with a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tvsnet aggregated two view stereo network for multi view stereo depth estimation")
  Member "efficient likelihood learning of a generic cnn crf model for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  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)