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: "post training 4 bit quantization of convolution networks for rapid deployment"
  Abstract: Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources. Neural network quantization has significant benefits in reducing the amount of intermediate results, but it often requires the full datasets and time-consuming fine tuning to recover the accuracy lost after quantization. This paper introduces the first practical 4-bit post training quantization approach: it does not involve training the quantized model (fine-tuning), nor it requires the availability of the full dataset. We target the quantization of both activations and weights and suggest three complementary methods for minimizing quantization error at the tensor level, two of whom obtain a closed-form analytical solution. Combining these methods, our approach achieves accuracy that is just a few percents less the state-of-the-art baseline across a wide range of convolutional models. The source code to replicate all experiments is available on GitHub: \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "towards unified int8 training for convolutional neural network"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "learned step size quantization"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-2 (induced by source paper "differentiable soft quantization bridging full precision and low bit neural networks"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "releq an automatic reinforcement learning approach for deep quantization of neural networks"

Connected through hyperedge HE-3 (induced by source paper "dsconv efficient convolution operator"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "training dnns with hybrid block floating point"

Connected through hyperedge HE-4 (induced by source paper "ir net forward and backward information retention for highly accurate binary neural networks"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "regularizing activation distribution for training binarized deep networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "low bit quantization of neural networks for efficient inference"):
  with members:
    - "model compression via distillation and quantization"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning discrete weights using the local reparameterization trick"

Connected through hyperedge HE-6 (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"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "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"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-7 (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"
    - "quantizing convolutional neural networks for low power high throughput inference engines"
    - "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"
    - "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-8 (induced by source paper "post training 4 bit quantization on embedding tables"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "high accuracy low precision training"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "training quantized nets a deeper understanding"

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)