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: "sequence level knowledge distillation"
  Abstract: Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical approaches. However to reach competitive performance, NMT models need to be exceedingly large. In this paper we consider applying knowledge distillation approaches (Bucila et al., 2006; Hinton et al., 2015) that have proven successful for reducing the size of neural models in other domains to the problem of NMT. We demonstrate that standard knowledge distillation applied to word-level prediction can be effective for NMT, and also introduce two novel sequence-level versions of knowledge distillation that further improve performance, and somewhat surprisingly, seem to eliminate the need for beam search (even when applied on the original teacher model). Our best student model runs 10 times faster than its state-of-the-art teacher with little loss in performance. It is also significantly better than a baseline model trained without knowledge distillation: by 4.2/1.7 BLEU with greedy decoding/beam search. Applying weight pruning on top of knowledge distillation results in a student model that has 13 times fewer parameters than the original teacher model, with a decrease of 0.4 BLEU.

Connected through hyperedge HE-1 (induced by source paper "parallel machine translation with disentangled context transformer"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "cloze driven pretraining of self attention networks"
    - "fast structured decoding for sequence models"
    - "levenshtein transformer"

Connected through hyperedge HE-2 (induced by source paper "on the discrepancy between density estimation and sequence generation"):
  with members:
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "integer discrete flows and lossless compression"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "mixture models for diverse machine translation tricks of the trade"
    - "discrete flows invertible generative models of discrete data"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

Connected through hyperedge HE-3 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-4 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "privileged features distillation at taobao recommendations"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "large scale distributed neural network training through online distillation"
    - "self attentive sequential recommendation"
    - "deep mutual learning"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"

Connected through hyperedge HE-6 (induced by source paper "distill adapt distill training small in domain models for neural machine translation"):
  with members:
    - "go from the general to the particular multi domain translation with domain transformation networks"
    - "dreaming to distill data free knowledge transfer via deepinversion"
    - "iterative dual domain adaptation for neural machine translation"
    - "explaining sequence level knowledge distillation as data augmentation for neural machine translation"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "patient knowledge distillation for bert model compression"
    - "attentive student meets multi task teacher improved knowledge distillation for pretrained models"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "style example guided text generation using generative adversarial transformers"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "pretraining based natural language generation for text summarization"
    - "multi task deep neural networks for natural language understanding"
    - "domain adaptive text style transfer"
    - "defending against neural fake news"

Connected through hyperedge HE-8 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

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)