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: "contextual augmentation data augmentation by words with paradigmatic relations"
  Abstract: We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "voyageur an experiential travel search engine"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "subjective databases"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"

Connected through hyperedge HE-2 (induced by source paper "benchmarking popular classification models robustness to random and targeted corruptions"):
  with members:
    - "model vulnerability to distributional shifts over image transformation sets"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "textbugger generating adversarial text against real world applications"
    - "deep text classification can be fooled"
    - "deepmutation mutation testing of deep learning systems"
    - "generating natural language adversarial examples"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-3 (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-4 (induced by source paper "investigating an approach for low resource language dataset creation curation and classification setswana and sepedi"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "improving short text classification through global augmentation methods"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "enriching word vectors with subword information"
    - "transfer learning for low resource neural machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforced co training"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "banditsum extractive summarization as a contextual bandit"

Connected through hyperedge HE-6 (induced by source paper "improving the interpretability of neural sentiment classifiers via data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "attention is not explanation"
    - "generating token level explanations for natural language inference"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "transformation networks for target oriented sentiment classification"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "coreference resolution as query based span prediction"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "ellipsis and coreference resolution as question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "entity relation extraction as multi turn question answering"
    - "bert for coreference resolution baselines and analysis"
    - "gender bias in contextualized word embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"

Connected through hyperedge HE-8 (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"
    - "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"
    - "conditional bert contextual augmentation"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)