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: "conditional bert contextual augmentation"
  Abstract: We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked language model\footnote{The term "conditional masked language model" appeared once in original BERT paper, which indicates context-conditional, is equivalent to term "masked language model". In our paper, "conditional masked language model" indicates we apply extra label-conditional constraint to the "masked language model".} task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain obvious improvement.

Connected through hyperedge HE-1 (induced by source paper "data augmentation using pre trained transformer models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "the curious case of neural text degeneration"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "ctrl a conditional transformer language model for controllable generation"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning data manipulation for augmentation and weighting"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "learning data manipulation for augmentation and weighting"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "on learning intrinsic rewards for policy gradient methods"
    - "not all samples are created equal deep learning with importance sampling"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "tinybert distilling bert for natural language understanding"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "spanbert improving pre training by representing and predicting spans"
    - "revealing the dark secrets of bert"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-4 (induced by source paper "automatic fact guided sentence modification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bilinear attention networks"
    - "style transfer as unsupervised machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-5 (induced by source paper "sentilr linguistic knowledge enhanced language representation for sentiment analysis"):
  with members:
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "mass masked sequence to sequence pre training for language generation"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "story ending prediction by transferable bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-6 (induced by source paper "not enough data deep learning to the rescue"):
  with members:
    - "data augmentation using gans"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "improving short text classification through global augmentation methods"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "soft contextual data augmentation for neural machine translation"):
  with members:
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "autoaugment learning augmentation policies from data"
    - "data augmentation for low resource neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "data noising as smoothing in neural network language models"

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)