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 "fast and differentiable message passing for stereo vision"):
  Abstract: Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-crafted model parameters. In addition to differentiability, the two main aspects that enable learning these model parameters are the forward and backward propagation time of the MRF optimization algorithm and its parallelization capabilities. In this work, we introduce two fast and differentiable message passing algorithms, namely, Iterative Semi-Global Matching Revised (ISGMR) and Parallel Tree-Reweighted Message Passing (TRWP) which are greatly sped up on GPU by exploiting massive parallelism. Specifically, ISGMR is an iterative and revised version of the standard SGM for general second-order MRFs with improved optimization effectiveness, whereas TRWP is a highly parallelizable version of Sequential TRW (TRWS) for faster optimization. Our experiments on standard stereo benchmarks demonstrate that ISGMR achieves much lower energies than SGM and TRWP is two orders of magnitude faster than TRWS without losing effectiveness in optimization. Furthermore, our CUDA implementations are at least 7 and 650 times faster than PyTorch GPU implementations in the forward and backward propagation, respectively, enabling efficient end-to-end learning with message passing.

Includes members:
  Member "ga net guided aggregation net for end to end stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "generalized range moves":
    also connected through other hyperedges:
      (none)
  Member "memory efficient max flow for multi label submodular mrfs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient graph cut optimization for full crfs with quantized edges")
  Member "embedded real time stereo estimation via semi global matching on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust full fov depth estimation in tele wide camera system")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "iteratively reweighted graph cut for multi label mrfs with non convex priors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deeply supervised depth map super resolution as novel view synthesis")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "learning graphical model parameters with approximate marginal inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")

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)