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: "weight normalization based quantization for deep neural network compression"
  Abstract: With the development of deep neural networks, the size of network models becomes larger and larger. Model compression has become an urgent need for deploying these network models to mobile or embedded devices. Model quantization is a representative model compression technique. Although a lot of quantization methods have been proposed, many of them suffer from a high quantization error caused by a long-tail distribution of network weights. In this paper, we propose a novel quantization method, called weight normalization based quantization (WNQ), for model compression. WNQ adopts weight normalization to avoid the long-tail distribution of network weights and subsequently reduces the quantization error. Experiments on CIFAR-100 and ImageNet show that WNQ can outperform other baselines to achieve state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks"):
  with members:
    - "knowledge distillation for optimization of quantized deep neural networks"
    - "quantization networks"
    - "swalp stochastic weight averaging in low precision training"
    - "empirical analysis of knowledge distillation technique for optimization of quantized deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"

Connected through hyperedge HE-2 (induced by source paper "additive powers of two quantization an efficient non uniform discretization for neural networks"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "learned step size quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "additive powers of two quantization a non uniform discretization for neural networks"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "learned step size quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "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)