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: "reducing transformer depth on demand with structured dropout"
  Abstract: Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation.

Connected through hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers"):
  with members:
    - "towards stabilizing batch statistics in backward propagation of batch normalization"
    - "improving deep transformer with depth scaled initialization and merged attention"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a tensorized transformer for language modeling"
    - "root mean square layer normalization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "understanding and improving layer normalization"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-2 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "augmenting self attention with persistent memory"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-3 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "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"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-4 (induced by source paper "controlling computation versus quality for neural sequence models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "mixture models for diverse machine translation tricks of the trade"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "universal transformers"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-5 (induced by source paper "further boosting bert based models by duplicating existing layers some intriguing phenomena inside bert"):
  with members:
    - "are sixteen heads really better than one"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "designing and interpreting probes with control tasks"
    - "attention is not explanation"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "a multiscale visualization of attention in the transformer model"

Connected through hyperedge HE-6 (induced by source paper "scaling up online speech recognition using convnets"):
  with members:
    - "rnn t for latency controlled asr with improved beam search"
    - "a comparative study on transformer vs rnn in speech applications"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "transformer based acoustic modeling for hybrid speech recognition"
    - "streaming end to end speech recognition for mobile devices"
    - "fully convolutional speech recognition"
    - "efficient neural audio synthesis"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-7 (induced by source paper "flaubert unsupervised language model pre training for french"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "very deep self attention networks for end to end speech recognition"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"
    - "paws paraphrase adversaries from word scrambling"

Connected through hyperedge HE-8 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "word level speech recognition with a dynamic lexicon"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "a fully differentiable beam search decoder"

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)