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: "tinybert distilling bert for natural language understanding"
  Abstract: Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive and memory intensive, so it is difficult to effectively execute them on some resource-restricted devices. To accelerate inference and reduce model size while maintaining accuracy, we firstly propose a novel transformer distillation method that is a specially designed knowledge distillation (KD) method for transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large teacher BERT can be well transferred to a small student TinyBERT. Moreover, we introduce a new two-stage learning framework for TinyBERT, which performs transformer distillation at both the pre-training and task-specific learning stages. This framework ensures that TinyBERT can capture both the general-domain and task-specific knowledge of the teacher BERT.TinyBERT is empirically effective and achieves more than 96% the performance of teacher BERTBASE on GLUE benchmark while being 7.5x smaller and 9.4x faster on inference. TinyBERT is also significantly better than state-of-the-art baselines on BERT distillation, with only about 28% parameters and about 31% inference time of them.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "knowledge distillation from internal representations"
    - "small and practical bert models for sequence labeling"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "cloze driven pretraining of self attention networks"

Connected through hyperedge HE-3 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search"):
  with members:
    - "q8bert quantized 8bit bert"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "deep double descent where bigger models and more data hurt"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-5 (induced by source paper "twinbert distilling knowledge to twin structured bert models for efficient retrieval"):
  with members:
    - "well read students learn better on the importance of pre training compact models"
    - "multi task deep neural networks for natural language understanding"
    - "model compression with multi task knowledge distillation for web scale question answering system"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "distilling task specific knowledge from bert into simple neural networks"

Connected through hyperedge HE-6 (induced by source paper "textbrewer an open source knowledge distillation toolkit for natural language processing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "extreme language model compression with optimal subwords and shared projections"
    - "multilingual neural machine translation with knowledge distillation"
    - "albert a lite bert for self supervised learning of language representations"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "pre training with whole word masking for chinese bert"
    - "bam born again multi task networks for natural language understanding"
    - "preparing lessons improve knowledge distillation with better supervision"

Connected through hyperedge HE-7 (induced by source paper "aligning the pretraining and finetuning objectives of language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-8 (induced by source paper "improving bert fine tuning via self ensemble and self distillation"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "parameter efficient transfer learning for nlp"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "how to fine tune bert for text classification"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "roberta a robustly optimized bert pretraining approach"
    - "unified language model pre training for natural language understanding and generation"

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)