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: "freelb enhanced adversarial training for language understanding"
  Abstract: Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm - FreeLB, that promotes higher robustness and invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples. To validate the effectiveness of the proposed approach, we apply it to Transformer-based models for natural language understanding and commonsense reasoning tasks. Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores of BERT-based model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8. In addition, the proposed approach achieves state-of-the-art single-model test accuracies of 85.44% and 67.75% on ARC-Easy and ARC-Challenge. Experiments on CommonsenseQA benchmark further demonstrate that FreeLB can be generalized and boost the performance of RoBERTa-large model on other tasks as well.

Connected through hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multi task deep neural networks for natural language understanding"
    - "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "ernie 2 0 a continual pre training framework for language understanding"

Connected through hyperedge HE-2 (induced by source paper "maxup a simple way to improve generalization of neural network training"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "improving neural language modeling via adversarial training"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial examples improve image recognition"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "fine tuning pretrained language models weight initializations data orders and early stopping"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "show your work improved reporting of experimental results"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"

Connected through hyperedge HE-4 (induced by source paper "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "parameter efficient transfer learning for nlp"
    - "spanbert improving pre training by representing and predicting spans"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "on the variance of the adaptive learning rate and beyond"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-5 (induced by source paper "deepening hidden representations from pre trained language models for natural language understanding"):
  with members:
    - "semantics aware bert for language understanding"
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"

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)