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: "neuroner an easy to use program for named entity recognition based on neural networks"
  Abstract: Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easy-to-use named-entity recognition tool based on ANNs. Users can annotate entities using a graphical web-based user interface (BRAT): the annotations are then used to train an ANN, which in turn predict entities' locations and categories in new texts. NeuroNER makes this annotation-training-prediction flow smooth and accessible to anyone.

Connected through hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  with members:
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "medical concept embedding with time aware attention"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"

Connected through hyperedge HE-2 (induced by source paper "representation learning for electronic health records"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multimodal multitask representation learning for pathology biobank metadata prediction"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "clinical concept extraction for document level coding"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "unsupervised clinical language translation"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-3 (induced by source paper "a survey on recent advances in named entity recognition from deep learning models"):
  with members:
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "an investigation of recurrent neural architectures for drug name recognition"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "transfer learning for sequence labeling using source model and target data"):
  with members:
    - "progressive neural networks"
    - "named entity recognition for novel types by transfer learning"
    - "how transferable are neural networks in nlp applications"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "adam a method for stochastic optimization"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-5 (induced by source paper "error analysis for vietnamese named entity recognition on deep neural network models"):
  with members:
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-6 (induced by source paper "extraction and analysis of clinically important follow up recommendations in a large radiology dataset"):
  with members:
    - "transfer learning for named entity recognition with neural networks"
    - "de identification of patient notes with recurrent neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "automatic end to end de identification is high accuracy the only metric"):
  with members:
    - "a survey of automatic de identification of longitudinal clinical narratives"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "transfer learning for named entity recognition with neural networks"

Connected through hyperedge HE-8 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

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)