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 "training for unstable cnn accelerator a case study on fpga"):
  Abstract: With the great advancements of convolution neural networks(CNN), CNN accelerators are increasingly developed and deployed in the major computing this http URL make use of the CNN accelerators, CNN models are trained via the off-line training systems such as Caffe, Pytorch and Tensorflow on multi-core CPUs and GPUs first and then compiled to the target accelerators. Although the two-step process seems to be natural and has been widely applied, it assumes that the accelerators' behavior can be fully modeled on CPUs and GPUs. This does not hold true and the behavior of the CNN accelerators is un-deterministic when the circuit works at 'unstable' mode when it is overclocked or is affected by the environment like fault-prone aerospace. The exact behaviors of the accelerators are determined by both the chip fabrication and the working environment or status. In this case, applying the conventional off-line training result to the accelerators directly may lead to considerable accuracy loss. #R##N#To address this problem, we propose to train for the 'unstable' CNN accelerator and have the 'un-determined behavior' learned together with the data in the same framework. Basically, it starts from the off-line trained model and then integrates the uncertain circuit behaviors into the CNN models through additional accelerator-specific training. The fine-tuned training makes the CNN models less sensitive to the circuit uncertainty. We apply the design method to both an overclocked CNN accelerator and a faulty accelerator. According to our experiments on a subset of ImageNet, the accelerator-specific training can improve the top 5 accuracy up to 3.4% and 2.4% on average when the CNN accelerator is at extreme overclocking. When the accelerator is exposed to a faulty environment, the top 5 accuracy improves up to 6.8% and 4.28% on average under the most severe fault injection.

Includes members:
  Member "caffeinated fpgas fpga framework for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gemmini an agile systolic array generator enabling systematic evaluations of deep learning architectures")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "fixed point quantization of deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  Member "neural networks with few multiplications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "training deep neural networks with low precision multiplications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "compressing deep convolutional networks using vector quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)