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: "solving dense image matching in real time using discrete continuous optimization"
  Abstract: Dense image matching is a fundamental low-level problem in Computer Vision, which has received tremendous attention from both discrete and continuous optimization communities. The goal of this paper is to combine the advantages of discrete and continuous optimization in a coherent framework. We devise a model based on energy minimization, to be optimized by both discrete and continuous algorithms in a consistent way. In the discrete setting, we propose a novel optimization algorithm that can be massively parallelized. In the continuous setting we tackle the problem of non-convex regularizers by a formulation based on differences of convex functions. The resulting hybrid discrete-continuous algorithm can be efficiently accelerated by modern GPUs and we demonstrate its real-time performance for the applications of dense stereo matching and optical flow.

Connected through hyperedge HE-1 (induced by source paper "learned collaborative stereo refinement"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "learning energy based inpainting for optical flow"
    - "pyramid stereo matching network"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "end to end training of hybrid cnn crf models for stereo"
    - "atgv net accurate depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "bottleneck potentials in markov random fields"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "probabilistic intra retinal layer segmentation in 3 d oct images using global shape regularization"
    - "combining the shortest paths and the bottleneck paths problems"

Connected through hyperedge HE-3 (induced by source paper "scalable full flow with learned binary descriptors"):
  with members:
    - "accurate optical flow via direct cost volume processing"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "end to end training of hybrid cnn crf models for stereo"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "computing the stereo matching cost with a convolutional neural network"
    - "patchbatch a batch augmented loss for optical flow"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-4 (induced by source paper "end to end training of hybrid cnn crf models for stereo"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "complexity of discrete energy minimization problems"
    - "semantic image segmentation via deep parsing network"
    - "computing the stereo matching cost with a convolutional neural network"
    - "efficient likelihood learning of a generic cnn crf model for semantic segmentation"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "full flow optical flow estimation by global optimization over regular grids"):
  with members:
    - "optical flow with semantic segmentation and localized layers"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

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)