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 "asv accelerated stereo vision system"):
  Abstract: Estimating depth from stereo vision cameras, i.e., "depth from stereo", is critical to emerging intelligent applications deployed in energy- and performance-constrained devices, such as augmented reality headsets and mobile autonomous robots. While existing stereo vision systems make trade-offs between accuracy, performance and energy-efficiency, we describe ASV, an accelerated stereo vision system that simultaneously improves both performance and energy-efficiency while achieving high accuracy.   The key to ASV is to exploit unique characteristics inherent to stereo vision, and apply stereo-specific optimizations, both algorithmically and computationally. We make two contributions. Firstly, we propose a new stereo algorithm, invariant-based stereo matching (ISM), that achieves significant speedup while retaining high accuracy. The algorithm combines classic "hand-crafted" stereo algorithms with recent developments in Deep Neural Networks (DNNs), by leveraging the correspondence invariant unique to stereo vision systems. Secondly, we observe that the bottleneck of the ISM algorithm is the DNN inference, and in particular the deconvolution operations that introduce massive compute-inefficiencies. We propose a set of software optimizations that mitigate these inefficiencies. We show that with less than 0.5% hardware area overhead, these algorithmic and computational optimizations can be effectively integrated within a conventional DNN accelerator. Overall, ASV achieves 5× speedup and 85% energy saving with 0.02% accuracy loss compared to today's DNN-based stereo vision systems.

Includes members:
  Member "fixynn efficient hardware for mobile computer vision via transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks")
  Member "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing")
  Member "dnn dataflow choice is overrated":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures")
  Member "morph flexible acceleration for 3d cnn based video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference")
  Member "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")
  Member "euphrates algorithm soc co design for low power mobile continuous vision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "balancing efficiency and flexibility for dnn acceleration via temporal gpu systolic array integration")
  Member "scale sim systolic cnn accelerator simulator":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures")
  Member "mobile machine learning hardware at arm a systems on chip soc perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")

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)