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: "probing biomedical embeddings from language models"
  Abstract: Contextualized word embeddings derived from pre-trained language models (LMs) show significant improvements on downstream NLP tasks. Pre-training on domain-specific corpora, such as biomedical articles, further improves their performance. In this paper, we conduct probing experiments to determine what additional information is carried intrinsically by the in-domain trained contextualized embeddings. For this we use the pre-trained LMs as fixed feature extractors and restrict the downstream task models to not have additional sequence modeling layers. We compare BERT, ELMo, BioBERT and BioELMo, a biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We use visualization and nearest neighbor analysis to show that better encoding of entity-type and relational information leads to this superiority.

Connected through hyperedge HE-1 (induced by source paper "parsing early modern english for linguistic search"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence tagging with bidirectional language models"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "ml net multi label classification of biomedical texts with deep neural networks"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "publicly available clinical bert embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "pubmedqa a dataset for biomedical research question answering"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "interpretation of natural language rules in conversational machine reading"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "deep contextualized word representations"
    - "emrqa a large corpus for question answering on electronic medical records"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "incorporating domain knowledge into medical nli using knowledge graphs"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "deep contextualized word representations"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural natural language inference models enhanced with external knowledge"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "knowledge base completion baselines strike back"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "deep contextualized biomedical abbreviation expansion"):
  with members:
    - "unsupervised abbreviation disambiguation contextual disambiguation using word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings and recurrent neural networks based on long short term memory nodes in supervised biomedical word sense disambiguation"
    - "adam a method for stochastic optimization"

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)