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: "efficient 8 bit quantization of transformer neural machine language translation model"
  Abstract: In this work, we quantize a trained Transformer machine language translation model leveraging INT8/VNNI instructions in the latest Intel$^\circledR$ Xeon$^\circledR$ Cascade Lake processors to improve inference performance while maintaining less than 0.5$\%$ drop in accuracy. To the best of our knowledge, this is the first attempt in the industry to quantize the Transformer model. This has high impact as it clearly demonstrates the various complexities of quantizing the language translation model. We present novel quantization techniques directly in TensorFlow to opportunistically replace 32-bit floating point (FP32) computations with 8-bit integers (INT8) and transform the FP32 computational graph. We also present a bin-packing parallel batching technique to maximize CPU utilization. Overall, our optimizations with INT8/VNNI deliver 1.5X improvement over the best FP32 performance. Furthermore, it reveals the opportunities and challenges to boost performance of quantized deep learning inference and establishes best practices to run inference with high efficiency on Intel CPUs.

Connected through hyperedge HE-1 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "language modeling with deep transformers"
    - "fully quantized transformer for improved translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "streaming end to end speech recognition for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "learning accurate integer transformer machine translation models"):
  with members:
    - "asymmetric valleys beyond sharp and flat local minima"
    - "training tips for the transformer model"
    - "marian cost effective high quality neural machine translation in c"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-3 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

Connected through hyperedge HE-4 (induced by source paper "qkd quantization aware knowledge distillation"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "feature fusion for online mutual knowledge distillation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learned step size quantization"

Connected through hyperedge HE-5 (induced by source paper "q bert hessian based ultra low precision quantization of bert"):
  with members:
    - "are sixteen heads really better than one"
    - "a tensorized transformer for language modeling"
    - "lightweight and efficient neural natural language processing with quaternion networks"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-6 (induced by source paper "fully quantized transformer for improved translation"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "weighted transformer network for machine translation"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "adaptivfloat a floating point based data type for resilient deep learning inference"):
  with members:
    - "deep positron a deep neural network using the posit number system"
    - "effective quantization approaches for recurrent neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "wrpn wide reduced precision networks"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "q8bert quantized 8bit bert"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "attention is all you need"
    - "squad 100 000 questions for machine comprehension of text"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "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.LG(Machine Learning)