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: "uniq uniform noise injection for non uniform quantization of neural networks"
  Abstract: We present a novel method for neural network quantization that emulates a non-uniform $k$-quantile quantizer, which adapts to the distribution of the quantized parameters. Our approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We suggest to compare the results as a function of the bit-operations (BOPS) performed, assuming a look-up table availability for the non-uniform case. In this setup, we show the advantages of our strategy in the low computational budget regime. While the proposed solution is harder to implement in hardware, we believe it sets a basis for new alternatives to neural networks quantization.

Connected through hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-2 (induced by source paper "near lossless post training quantization of deep neural networks via a piecewise linear approximation"):
  with members:
    - "low bit quantization of neural networks for efficient inference"
    - "stochastic activation pruning for robust adversarial defense"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning with low precision by half wave gaussian quantization"
    - "more is less a more complicated network with less inference complexity"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-4 (induced by source paper "differentiable soft quantization bridging full precision and low bit neural networks"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "releq an automatic reinforcement learning approach for deep quantization of neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-5 (induced by source paper "feature map transform coding for energy efficient cnn inference"):
  with members:
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "regularizing activation distribution for training binarized deep networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "swalp stochastic weight averaging in low precision training"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "faster gaze prediction with dense networks and fisher pruning"

Connected through hyperedge HE-6 (induced by source paper "fighting quantization bias with bias"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "uniq uniform noise injection for the quantization of neural networks"
    - "a quantization friendly separable convolution for mobilenets"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-7 (induced by source paper "scalable model compression by entropy penalized reparameterization"):
  with members:
    - "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
    - "uniq uniform noise injection for the quantization of neural networks"
    - "relaxed quantization for discretized neural networks"
    - "entropy constrained training of deep neural networks"
    - "coreset based neural network compression"
    - "end to end optimized image compression"
    - "ternary weight networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "bi real net binarizing deep network towards real network performance"):
  with members:
    - "uniq uniform noise injection for the quantization of neural networks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "towards accurate binary convolutional neural network"

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.LG(Machine Learning)