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: "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
  Abstract: Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We make the datasets, pre-trained models, and codes publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "open event extraction from online text using a generative adversarial network"
    - "analyzing and improving the image quality of stylegan"
    - "generative adversarial network in medical imaging a review"
    - "a bert based transfer learning approach for hate speech detection in online social media"
    - "image generation from small datasets via batch statistics adaptation"
    - "mind2mind transfer learning for gans"

Connected through hyperedge HE-2 (induced by source paper "cbag conditional biomedical abstract generation"):
  with members:
    - "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"
    - "language models as knowledge bases"
    - "the deepfake detection challenge dfdc preview dataset"
    - "ernie enhanced representation through knowledge integration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "bert has a mouth and it must speak bert as a markov random field language model"

Connected through hyperedge HE-3 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "passage re ranking with bert"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "roberta a robustly optimized bert pretraining approach"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "content based citation recommendation"

Connected through hyperedge HE-4 (induced by source paper "the utility of general domain transfer learning for medical language tasks"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"

Connected through hyperedge HE-5 (induced by source paper "extracting umls concepts from medical text using general and domain specific deep learning models"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "publicly available clinical bert embeddings"
    - "joint entity extraction and assertion detection for clinical text"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"

Connected through hyperedge HE-6 (induced by source paper "end to end named entity recognition and relation extraction using pre trained language models"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "entity relation extraction as multi turn question answering"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "end to end neural relation extraction using deep biaffine attention"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "towards understanding of medical randomized controlled trials by conclusion generation"):
  with members:
    - "doubletransfer at mediqa 2019 multi source transfer learning for natural language understanding in the medical domain"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "on the automatic generation of medical imaging reports"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "learning to summarize radiology findings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-8 (induced by source paper "chemical protein interaction extraction via gaussian probability distribution and external biomedical knowledge"):
  with members:
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "attention is all you need"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

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)