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: "differentiable fine grained quantization for deep neural network compression"
  Abstract: Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted. Binary quantization obtains the highest compression but usually results in big accuracy drop. In practice, 8-bit or 16-bit quantization is often used aiming at maintaining the same accuracy as the original 32-bit precision. We observe different layers have different accuracy sensitivity of quantization. Thus judiciously selecting different precision for different layers/structures can potentially produce more efficient models compared to traditional quantization methods by striking a better balance between accuracy and compression rate. In this work, we propose a fine-grained quantization approach for deep neural network compression by relaxing the search space of quantization bitwidth from discrete to a continuous domain. The proposed approach applies gradient descend based optimization to generate a mixed-precision quantization scheme that outperforms the accuracy of traditional quantization methods under the same compression rate.

Connected through hyperedge HE-1 (induced by source paper "non structured dnn weight pruning is it beneficial in any platform"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "adversarial robustness vs model compression or both"
    - "hypar towards hybrid parallelism for deep learning accelerator array"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "morph flexible acceleration for 3d cnn based video understanding"

Connected through hyperedge HE-2 (induced by source paper "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"):
  with members:
    - "demystifying neural network filter pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"

Connected through hyperedge HE-3 (induced by source paper "semi relaxed quantization with dropbits training low bit neural networks via bit wise regularization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "relaxed quantization for discretized neural networks"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "heterogeneous bitwidth binarization in convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "learning discrete weights using the local reparameterization trick"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-4 (induced by source paper "toward extremely low bit and lossless accuracy in dnns with progressive admm"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "learning structured sparsity in deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "fixed point quantization of deep convolutional networks"
    - "learning both weights and connections for efficient neural networks"

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)