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: "learning low precision neural networks without straight through estimator ste"
  Abstract: The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding. We propose an alternative methodology called alpha-blending (AB), which quantizes neural networks to low-precision using stochastic gradient descent (SGD). Our method (AB) avoids STE approximation by replacing the quantized weight in the loss function by an affine combination of the quantized weight w_q and the corresponding full-precision weight w with non-trainable scalar coefficient $\alpha$ and $1-\alpha$. During training, $\alpha$ is gradually increased from 0 to 1; the gradient updates to the weights are through the full-precision term, $(1-\alpha)w$, of the affine combination; the model is converted from full-precision to low-precision progressively. To evaluate the method, a 1-bit BinaryNet on CIFAR10 dataset and 8-bits, 4-bits MobileNet v1, ResNet_50 v1/2 on ImageNet dataset are trained using the alpha-blending approach, and the evaluation indicates that AB improves top-1 accuracy by 0.9%, 0.82% and 2.93% respectively compared to the results of STE based quantization.

Connected through hyperedge HE-1 (induced by source paper "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"):
  with members:
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "low bit quantization of neural networks for efficient inference"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "ifq net integrated fixed point quantization networks for embedded vision"
    - "learned step size quantization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "mixed precision dnns all you need is a good parametrization"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learned step size quantization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxquant quantized neural networks via proximal operators"
    - "relaxed quantization for discretized neural networks"
    - "iteratively training look up tables for network quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "a survey of fpga based neural network accelerator"

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)