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: "patient knowledge distillation for bert model compression"
  Abstract: Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks. However, the high demand for computing resources in training such models hinders their application in practice. In order to alleviate this resource hunger in large-scale model training, we propose a Patient Knowledge Distillation approach to compress an original large model (teacher) into an equally-effective lightweight shallow network (student). Different from previous knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model patiently learns from multiple intermediate layers of the teacher model for incremental knowledge extraction, following two strategies: ($i$) PKD-Last: learning from the last $k$ layers; and ($ii$) PKD-Skip: learning from every $k$ layers. These two patient distillation schemes enable the exploitation of rich information in the teacher's hidden layers, and encourage the student model to patiently learn from and imitate the teacher through a multi-layer distillation process. Empirically, this translates into improved results on multiple NLP tasks with significant gain in training efficiency, without sacrificing model accuracy.

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 "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "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"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-3 (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-4 (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-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 "power bert accelerating bert inference for classification tasks"):
  with members:
    - "are sixteen heads really better than one"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "adaptively sparse transformers"
    - "q bert hessian based ultra low precision quantization of bert"
    - "sparse sequence to sequence models"
    - "explicit sparse transformer concentrated attention through explicit selection"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-7 (induced by source paper "trans blstm transformer with bidirectional lstm for language understanding"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (induced by source paper "subclass distillation"):
  with members:
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"
    - "self training with noisy student improves imagenet classification"
    - "paraphrasing complex network network compression via factor transfer"
    - "large scale distributed neural network training through online distillation"

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)