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: "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
  Abstract: Deep quantization of neural networks (below eight bits) offers significant promise in reducing their compute and storage cost. Albeit alluring, without special techniques for training and optimization, deep quantization results in significant accuracy loss. To further mitigate this loss, we propose a novel sinusoidal regularization, called SinReQ1, for deep quantized training. SinReQ adds a periodic term to the original objective function of the underlying training algorithm. SinReQ exploits the periodicity, differentiability, and the desired convexity profile in sinusoidal functions to automatically propel weights towards values that are inherently closer to quantization levels. Since, this technique does not require invasive changes to the training procedure, SinReQ can harmoniously enhance quantized training algorithms. SinReQ offers generality and flexibility as it is not limited to a certain bitwidth or a uniform assignment of bitwidths across layers. We carry out experimentation using the AlexNet, CIFAR-10, ResNet-18, ResNet-20, SVHN, and VGG-11 DNNs with three to five bits for quantization and show the versatility of SinReQ in enhancing multiple quantized training algorithms, DoReFa [32] and WRPN [24]. Averaging across all the bit configurations shows that SinReQ closes the accuracy gap between these two techniques and the full-precision runs by 32.4% and 27.5%, respectively. That is improving the absolute accuracy of DoReFa and WRPN by 2.8% and 2.1%, respectively.

Connected through hyperedge HE-1 (induced by source paper "gradient based deep quantization of neural networks through sinusoidal adaptive regularization"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "mixed precision dnns all you need is a good parametrization"
    - "learned step size quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning low precision deep neural networks through regularization"
    - "loss aware weight quantization of deep networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-2 (induced by source paper "robust quantization one model to rule them all"):
  with members:
    - "bit efficient quantization for deep neural networks"
    - "gdrq group based distribution reshaping for quantization"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "neural network distiller a python package for dnn compression research"
    - "fighting quantization bias with bias"
    - "learned step size quantization"

Connected through hyperedge HE-3 (induced by source paper "neural network distiller a python package for dnn compression research"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "attention based guided structured sparsity of deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "wrpn wide reduced precision 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.LG(Machine Learning)