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: "collabonet collaboration of deep neural networks for biomedical named entity recognition"
  Abstract: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.

Connected through hyperedge HE-1 (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"
    - "probing biomedical embeddings from language models"
    - "ml net multi label classification of biomedical texts with deep neural networks"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "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-2 (induced by source paper "scibert a pretrained language model for scientific text"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

Connected through hyperedge HE-3 (induced by source paper "biobert a pre trained biomedical language representation model for biomedical text mining"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "a silver standard corpus of human phenotype gene relations"
    - "publicly available clinical bert embeddings"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "pre trained language model for biomedical question answering"):
  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"
    - "using neural networks for relation extraction from biomedical literature"
    - "publicly available clinical bert embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

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)