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: "quantizing convolutional neural networks for low power high throughput inference engines"
  Abstract: Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly insatiable appetites for computational resources not only while training, but also when deployed at scales ranging from data centers all the way down to embedded devices. As such, increasing consideration is being made to maximize the computational efficiency given limited hardware and energy resources and, as a result, inferencing with reduced precision has emerged as a viable alternative to the IEEE 754 Standard for Floating-Point Arithmetic. We propose a quantization scheme that allows inferencing to be carried out using arithmetic that is fundamentally more efficient when compared to even half-precision floating-point. Our quantization procedure is significant in that we determine our quantization scheme parameters by calibrating against its reference floating-point model using a single inference batch rather than (re)training and achieve end-to-end post quantization accuracies comparable to the reference model.

Connected through hyperedge HE-1 (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-2 (induced by source paper "improving neural network quantization without retraining using outlier channel splitting"):
  with members:
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "towards effective low bitwidth convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "composite binary decomposition networks"):
  with members:
    - "network decoupling from regular to depthwise separable convolutions"
    - "progressive neural architecture search"
    - "binary decomposed dcnn for accelerating computation and compressing model without retraining"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning accurate low bit deep neural networks with stochastic quantization"
    - "dsod learning deeply supervised object detectors from scratch"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "rethinking machine learning development and deployment for edge devices"):
  with members:
    - "not all ops are created equal"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "bit fusion bit level dynamically composable architecture for accelerating deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)