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: "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"
  Abstract: Biomedical named entity recognition (NER) is a fundamental task in text mining of medical documents and has many applications. Deep learning based approaches to this task have been gaining increasing attention in recent years as their parameters can be learned end-to-end without the need for hand-engineered features. However, these approaches rely on high-quality labeled data, which is expensive to obtain. To address this issue, we investigate how to use unlabeled text data to improve the performance of NER models. Specifically, we train a bidirectional language model (BiLM) on unlabeled data and transfer its weights to "pretrain" an NER model with the same architecture as the BiLM, which results in a better parameter initialization of the NER model. We evaluate our approach on four benchmark datasets for biomedical NER and show that it leads to a substantial improvement in the F1 scores compared with the state-of-the-art approaches. We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.

Connected through hyperedge HE-1 (induced by source paper "mt bioner multi task learning for biomedical named entity recognition using deep bidirectional transformers"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multi task deep neural networks for natural language understanding"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "audio de identification a new entity recognition task"
    - "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"

Connected through hyperedge HE-2 (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"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "scispacy fast and robust models for biomedical natural language processing"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "construction of the literature graph in semantic scholar"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-4 (induced by source paper "deep transfer reinforcement learning for text summarization"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "dynamic multi level multi task learning for sentence simplification"

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)