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: "mixed low precision deep learning inference using dynamic fixed point"
  Abstract: We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy. In addition, we also constrain the activations to 8-bits thus enabling sub 8-bit full integer inference pipeline. Our method uses smaller clusters of N filters with a common scaling factor to minimize the quantization loss, while also maximizing the number of ternary operations. We show that with a cluster size of N=4 on Resnet-101, can achieve 71.8% TOP-1 accuracy, within 6% of the best full precision results while replacing ~85% of all multiplications with 8-bit accumulations. Using the same method with 4-bit weights achieves 76.3% TOP-1 accuracy which within 2% of the full precision result. We also study the impact of the size of the cluster on both performance and accuracy, larger cluster sizes N=64 can replace ~98% of the multiplications with ternary operations but introduces significant drop in accuracy which necessitates fine tuning the parameters with retraining the network at lower precision. To address this we have also trained low-precision Resnet-50 with 8-bit activations and ternary weights by pre-initializing the network with full precision weights and achieve 68.9% TOP-1 accuracy within 4 additional epochs. Our final quantized model can run on a full 8-bit compute pipeline, with a potential 16x improvement in performance compared to baseline full-precision models.

Connected through hyperedge HE-1 (induced by source paper "deepcabac a universal compression algorithm for deep neural networks"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "scalable methods for 8 bit training of neural networks"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "training deep neural networks with 8 bit floating point numbers"
    - "learning sparse neural networks via sensitivity driven regularization"
    - "universal deep neural network compression"

Connected through hyperedge HE-2 (induced by source paper "swalp stochastic weight averaging in low precision training"):
  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"
    - "high accuracy low precision training"
    - "training deep neural networks with 8 bit floating point numbers"
    - "averaging weights leads to wider optima and better generalization"
    - "training quantized nets a deeper understanding"
    - "computation error analysis of block floating point arithmetic oriented convolution neural network accelerator design"

Connected through hyperedge HE-3 (induced by source paper "smaller models better generalization"):
  with members:
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "noiseout a simple way to prune neural networks"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"
    - "learning both weights and connections for efficient neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "optimizing speech recognition for the edge"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "toward domain invariant speech recognition via large scale training"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "the unreasonable effectiveness of the forget gate"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-5 (induced by source paper "qpytorch a low precision arithmetic simulation framework"):
  with members:
    - "swalp stochastic weight averaging in low precision training"
    - "training and inference with integers in deep neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "tensorflow a system for large scale machine learning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep learning with limited numerical precision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "ngemm optimizing gemm for deep learning via compiler based techniques"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "fixed point quantization of deep convolutional networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "compact and computationally efficient representation of deep neural networks"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "methods for interpreting and understanding deep neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "universal deep neural network compression"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"

Connected through hyperedge HE-8 (induced by source paper "deploying customized data representation and approximate computing in machine learning applications"):
  with members:
    - "a hardware friendly algorithm for scalable training and deployment of dimensionality reduction models on fpga"
    - "fft based deep learning deployment in embedded systems"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "high performance fpga implementation of equivariant adaptive separation via independence algorithm for independent component analysis"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural networks"
    - "ternary weight 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)