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: "training high performance and large scale deep neural networks with full 8 bit integers"
  Abstract: Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simplify the operations for compute acceleration. Recently, researches on DNN quantization develop from inference to training, laying a foundation for the online training on accelerators. However, existing schemes leaving batch normalization (BN) untouched during training are mostly incomplete quantization that still adopts high precision FP in some parts of the data paths. Currently, there is no solution that can use only low bit-width INT data during the whole training process of large-scale DNNs with acceptable accuracy. In this work, through decomposing all the computation steps in DNNs and fusing three special quantization functions to satisfy the different precision requirements, we propose a unified complete quantization framework termed as ``WAGEUBN'' to quantize DNNs involving all data paths including W (Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN. Moreover, the Momentum optimizer is also quantized to realize a completely quantized framework. Experiments on ResNet18/34/50 models demonstrate that WAGEUBN can achieve competitive accuracy on the ImageNet dataset. For the first time, the study of quantization in large-scale DNNs is advanced to the full 8-bit INT level. In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency. Our throughout quantization framework has great potential for future efficient portable devices with online learning ability.

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"
    - "learned step size quantization"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "drawing early bird tickets towards more efficient training of deep networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "swalp stochastic weight averaging in low precision training"
    - "energy and policy considerations for deep learning in nlp"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"

Connected through hyperedge HE-3 (induced by source paper "panther a programmable architecture for neural network training harnessing energy efficient reram"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "a study of bfloat16 for deep learning training"
    - "training and inference with integers in deep neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "training deep neural networks with 8 bit floating point numbers"
    - "revisiting small batch training for deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mixed precision training"

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)