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: "extreme language model compression with optimal subwords and shared projections"
  Abstract: Pre-trained deep neural network language models such as ELMo, GPT, BERT and XLNet have recently achieved state-of-the-art performance on a variety of language understanding tasks. However, their size makes them impractical for a number of scenarios, especially on mobile and edge devices. In particular, the input word embedding matrix accounts for a significant proportion of the model's memory footprint, due to the large input vocabulary and embedding dimensions. Knowledge distillation techniques have had success at compressing large neural network models, but they are ineffective at yielding student models with vocabularies different from the original teacher models. We introduce a novel knowledge distillation technique for training a student model with a significantly smaller vocabulary as well as lower embedding and hidden state dimensions. Specifically, we employ a dual-training mechanism that trains the teacher and student models simultaneously to obtain optimal word embeddings for the student vocabulary. We combine this approach with learning shared projection matrices that transfer layer-wise knowledge from the teacher model to the student model. Our method is able to compress the BERT_BASE model by more than 60x, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7MB. Experimental results also demonstrate higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques.

Connected through hyperedge HE-1 (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"
    - "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 "textbrewer an open source knowledge distillation toolkit for natural language processing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "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"
    - "xlnet generalized autoregressive pretraining for language understanding"

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"
    - "dialogue transformers"

Connected through hyperedge HE-4 (induced by source paper "attentive student meets multi task teacher improved knowledge distillation for pretrained models"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "bam born again multi task networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "tinybert distilling bert for natural language understanding"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-5 (induced by source paper "the state of knowledge distillation for classification"):
  with members:
    - "relational knowledge distillation"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "a comprehensive overhaul of feature distillation"
    - "unsupervised data augmentation for consistency training"
    - "contrastive representation distillation"
    - "tinybert distilling bert for natural language understanding"
    - "patient knowledge distillation for bert model compression"

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)