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: "eda easy data augmentation techniques for boosting performance on text classification tasks"
  Abstract: We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.

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"
    - "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"
    - "combining mixmatch and active learning for better accuracy with fewer labels"

Connected through hyperedge HE-2 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dialog state tracking with reinforced data augmentation"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "user modeling for task oriented dialogues"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-3 (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"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "textbugger generating adversarial text against real world applications"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "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-4 (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"
    - "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"
    - "contextual augmentation data augmentation by words with paradigmatic relations"

Connected through hyperedge HE-5 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures"):
  with members:
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "where to put the image in an image caption generator"
    - "functional map of the world"
    - "remote sensing image scene classification benchmark and state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "investigating an approach for low resource language dataset creation curation and classification setswana and sepedi"):
  with members:
    - "improving short text classification through global augmentation methods"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "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-7 (induced by source paper "test metrics for recurrent neural networks"):
  with members:
    - "recurrent neural networks for time series forecasting"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "on adversarial examples for character level neural machine translation"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "symbolic execution for deep neural networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "improving the interpretability of neural sentiment classifiers via data augmentation"):
  with members:
    - "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"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "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"

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)