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 "deepshift towards multiplication less neural networks"):
  Abstract: Deployment of convolutional neural networks (CNNs) in mobile environments, their high computation and power budgets proves to be a major bottleneck. Convolution layers and fully connected layers, because of their intense use of multiplications, are the dominant contributer to this computation budget. This paper proposes to tackle this problem by introducing two new operations: convolutional shifts and fully-connected shifts, that replace multiplications all together with bitwise shift and sign flipping instead. For inference, both approaches may require only 6 bits to represent the weights. This family of neural network architectures (that use convolutional shifts and fully-connected shifts) are referred to as DeepShift models. We propose two methods to train DeepShift models: DeepShift-Q that trains regular weights constrained to powers of 2, and DeepShift-PS that trains the values of the shifts and sign flips directly. Training the DeepShift versions of ResNet18 architecture from scratch, we obtained accuracies of 92.33% on CIFAR10 dataset, and Top-1/Top-5 accuracies of 65.63%/86.33% on Imagenet dataset. Training the DeepShift version of VGG16 on ImageNet from scratch, resulted in a drop of less than 0.3% in Top-5 accuracy. Converting the pre-trained 32-bit floating point baseline model of GoogleNet to DeepShift and training it for 3 epochs, resulted in a Top-1/Top-5 accuracies of 69.87%/89.62% that are actually higher than that of the original model. Further testing is made on various well-known CNN architectures. Last but not least, we implemented the convolutional shifts and fully-connected shift GPU kernels and showed a reduction in latency time of 25\% when inferring ResNet18 compared to an unoptimized multiplication-based GPU kernels. The code is available online at this https URL.

Includes members:
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "syq learning symmetric quantization for efficient deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching for winograd aware quantized networks")
  Member "towards accurate binary convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep learning with low precision by half wave gaussian quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "switchable precision neural networks")
  Member "incremental network quantization towards lossless cnns with low precision weights":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.LG(Machine Learning)