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: "how to fine tune bert for text classification"
  Abstract: Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets.

Connected through hyperedge HE-1 (induced by source paper "calibration of pre trained transformers"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "calibration of encoder decoder models for neural machine translation"
    - "simplified neural unsupervised domain adaptation"
    - "evaluating lottery tickets under distributional shifts"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "revealing the dark secrets of bert"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition"):
  with members:
    - "mitigating gender bias in natural language processing literature review"
    - "huggingface s transformers state of the art natural language processing"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "counterfactual fairness in text classification through robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"

Connected through hyperedge HE-3 (induced by source paper "improving bert fine tuning via self ensemble and self distillation"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "parameter efficient transfer learning for nlp"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "tinybert distilling bert for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "unified language model pre training for natural language understanding and generation"

Connected through hyperedge HE-4 (induced by source paper "gradient based adversarial training on transformer networks for detecting check worthy factual claims"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "claimrank detecting check worthy claims in arabic and english"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "pel bert a joint model for protocol entity linking"):
  with members:
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "docbert bert for document classification"
    - "bridging the gap between training and inference for neural machine translation"
    - "claim extraction in biomedical publications using deep discourse model and transfer learning"
    - "deep contextualized word representations"
    - "leveraging textual specifications for grammar based fuzzing of network protocols"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "to tune or not to tune how about the best of both worlds"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep visual domain adaptation a survey"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "temporal film capturing long range sequence dependencies with feature wise modulations"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "dynamic layer normalization for adaptive neural acoustic modeling in speech recognition"
    - "film visual reasoning with a general conditioning layer"
    - "convolutional sequence to sequence learning"
    - "very deep convolutional networks for natural language processing"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "emergent properties of finetuned language representation models"):
  with members:
    - "q8bert quantized 8bit bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "q bert hessian based ultra low precision quantization of bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "revealing the dark secrets of bert"
    - "visualizing and understanding the effectiveness of bert"

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)