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 deep neural networks through regularization"
  Abstract: We consider the quantization of deep neural networks (DNNs) to produce low-precision models for efficient inference of fixed-point operations. Compared to previous approaches to training quantized DNNs directly under the constraints of low-precision weights and activations, we learn the quantization of DNNs with minimal quantization loss through regularization. In particular, we introduce the learnable regularization coefficient to find accurate low-precision models efficiently in training. In our experiments, the proposed scheme yields the state-of-the-art low-precision models of AlexNet and ResNet-18, which have better accuracy than their previously available low-precision models. We also examine our quantization method to produce low-precision DNNs for image super resolution. We observe only $0.5$~dB peak signal-to-noise ratio (PSNR) loss when using binary weights and 8-bit activations. The proposed scheme can be used to train low-precision models from scratch or to fine-tune a well-trained high-precision model to converge to a low-precision model. Finally, we discuss how a similar regularization method can be adopted in DNN weight pruning and compression, and show that $401\times$ compression is achieved for LeNet-5.

Connected through hyperedge HE-1 (induced by source paper "switchable precision neural networks"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "adaptative inference cost with convolutional neural mixture models"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "be your own teacher improve the performance of convolutional neural networks via self distillation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "searching for accurate binary neural architectures"
    - "auxiliary learning for deep multi task learning"

Connected through hyperedge HE-2 (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"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "learned step size quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-3 (induced by source paper "learned step size quantization"):
  with members:
    - "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"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "pact parameterized clipping activation for quantized neural networks"
    - "squeezenext hardware aware neural network design"

Connected through hyperedge HE-4 (induced by source paper "training high performance and large scale deep neural networks with full 8 bit integers"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "training and inference with integers in deep neural networks"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "training deep neural networks with 8 bit floating point numbers"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mixed precision training"

Connected through hyperedge HE-5 (induced by source paper "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "on periodic functions as regularizers for quantization of neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "visualizing the loss landscape of neural nets"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-6 (induced by source paper "learning multimodal fixed point weights using gradient descent"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "densely connected convolutional networks"
    - "trained ternary quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "very deep convolutional networks for large scale image recognition"

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.CV(Computer Vision and Pattern Recognition)