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: "cloze driven pretraining of self attention networks"
  Abstract: We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with the concurrently introduced BERT model. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.

Connected through hyperedge HE-1 (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"
    - "distilling task specific knowledge from bert into simple neural networks"

Connected through hyperedge HE-2 (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"
    - "fast structured decoding for sequence models"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

Connected through hyperedge HE-3 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a"):
  with members:
    - "named entity recognition with partially annotated training data"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "low resource name tagging learned with weakly labeled data"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "feature dependent confusion matrices for low resource ner labeling with noisy labels"
    - "massively multilingual transfer for ner"
    - "handling noisy labels for robustly learning from self training data for low resource sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "multi task deep neural networks for natural language understanding"
    - "provable guarantees for gradient based meta learning"
    - "characterizing and avoiding negative transfer"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-5 (induced by source paper "beheshti ner persian named entity recognition using bert"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "peyma a tagged corpus for persian named entities"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "unified language model pre training for natural language understanding and generation"):
  with members:
    - "fine tune bert for extractive summarization"
    - "learning and evaluating general linguistic intelligence"
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "cross lingual natural language generation via pre training"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "roberta a robustly optimized bert pretraining approach"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "mass masked sequence to sequence pre training for language generation"
    - "multi task deep neural networks for natural language understanding"
    - "defending against neural fake news"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"

Connected through hyperedge HE-8 (induced by source paper "effectiveness of self supervised pre training for speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "an unsupervised autoregressive model for speech representation learning"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"

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)