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: "cross type biomedical named entity recognition with deep multi task learning"
  Abstract: Motivation: Biomedical named entity recognition (BioNER) is the most fundamental task in biomedical text mining. State-of-the-art BioNER systems often require handcrafted features specifically designed for each type of biomedical entities. This feature generation process requires intensive labors from biomedical and linguistic experts, and makes it difficult to adapt these systems to new biomedical entity types. Although recent studies explored using neural network models for BioNER to free experts from manual feature generation, these models still require substantial human efforts to annotate massive training data. #R##N#Results: We propose a multi-task learning framework for BioNER that is based on neural network models to save human efforts. We build a global model by collectively training multiple models that share parameters, each model capturing the characteristics of a different biomedical entity type. In experiments on five BioNER benchmark datasets covering four major biomedical entity types, our model outperforms state-of-the-art systems and other neural network models by a large margin, even when only limited training data are available. Further analysis shows that the large performance gains come from sharing character- and word-level information between different biomedical entities. The approach creates new opportunities for text-mining approaches to help biomedical scientists better exploit knowledge in biomedical literature.

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"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

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"
    - "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-3 (induced by source paper "learning to contextually aggregate multi source supervision for sequence labeling"):
  with members:
    - "crossweigh training named entity tagger from imperfect annotations"
    - "massively multilingual transfer for ner"
    - "multinomial adversarial networks for multi domain text classification"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "semi supervised sequence modeling with cross view training"
    - "neural adaptation layers for cross domain named entity recognition"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "who said what modeling individual labelers improves classification"

Connected through hyperedge HE-4 (induced by source paper "progress notes classification and keyword extraction using attention based deep learning models with bert"):
  with members:
    - "publicly available clinical bert embeddings"
    - "deep learning for sentiment analysis a survey"
    - "fine tuned language models for text classification"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "very deep convolutional networks for text classification"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

Connected through hyperedge HE-5 (induced by source paper "knowledge guided named entity recognition"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "scibert a pretrained language model for scientific text"
    - "question answering is a format when is it useful"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "exploring ways to incorporate additional knowledge to improve natural language commonsense question answering"
    - "careful selection of knowledge to solve open book question answering"

Connected through hyperedge HE-6 (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"
    - "construction of the literature graph in semantic scholar"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-7 (induced by source paper "collabonet collaboration of deep neural networks for biomedical named entity recognition"):
  with members:
    - "recurrent neural network models for disease name recognition using domain invariant features"
    - "neural architectures for named entity recognition"
    - "character aware neural language models"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"):
  with members:
    - "learning named entity tagger using domain specific dictionary"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multi task domain adaptation for sequence tagging"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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.IR(Information Retrieval)