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: "q8bert quantized 8bit bert"
  Abstract: Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large amount of parameters. The emergence of even larger and more accurate models such as GPT2 and Megatron, suggest a trend of large pre-trained Transformer models. However, using these large models in production environments is a complex task requiring a large amount of compute, memory and power resources. In this work we show how to perform quantization-aware training during the fine-tuning phase of BERT in order to compress BERT by $4\times$ with minimal accuracy loss. Furthermore, the produced quantized model can accelerate inference speed if it is optimized for 8bit Integer supporting hardware.

Connected through hyperedge HE-1 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "extreme language model compression with optimal subwords and shared projections"
    - "bert rediscovers the classical nlp pipeline"
    - "q bert hessian based ultra low precision quantization of bert"
    - "structured pruning of large language models"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-2 (induced by source paper "efficient intent detection with dual sentence encoders"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "training neural response selection for task oriented dialogue systems"
    - "pretraining methods for dialog context representation learning"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a repository of conversational datasets"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "transfer fine tuning a bert case study"

Connected through hyperedge HE-3 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "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"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "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-4 (induced by source paper "dc bert decoupling question and document for efficient contextual encoding"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "retrieve read rerank towards end to end multi document reading comprehension"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"
    - "a discrete hard em approach for weakly supervised question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"

Connected through hyperedge HE-5 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "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"
    - "dialogue transformers"

Connected through hyperedge HE-6 (induced by source paper "emergent properties of finetuned language representation models"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "q bert hessian based ultra low precision quantization of bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "revealing the dark secrets of bert"
    - "visualizing and understanding the effectiveness of bert"
    - "how to fine tune bert for text classification"

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.CL(Computation and Language)