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: "transfer learning for named entity recognition with neural networks"
  Abstract: Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.

Connected through hyperedge HE-1 (induced by source paper "zero resource cross domain named entity recognition"):
  with members:
    - "do we need word order information for cross lingual sequence labeling"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "robust zero shot cross domain slot filling with example values"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "neural adaptation layers for cross domain named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "contextualized embeddings in named entity recognition an empirical study on generalization"):
  with members:
    - "how multilingual is multilingual bert"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "lexical features in coreference resolution to be used with caution"
    - "using linguistic features to improve the generalization capability of neural coreference resolvers"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "application of pre training models in named entity recognition"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "publicly available clinical bert embeddings"
    - "roberta a robustly optimized bert pretraining approach"
    - "attention is all you need"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "comparing rule based feature based and deep neural methods for de identification of dutch medical records"):
  with members:
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "de identification of patient notes with recurrent neural networks"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-5 (induced by source paper "a joint named entity recognizer for heterogeneous tag setsusing a tag hierarchy"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "an overview of multi task learning in deep neural networks"
    - "named entity recognition for novel types by transfer learning"
    - "de identification of patient notes with recurrent neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-6 (induced by source paper "pretrained language model transfer on neural named entity recognition in indonesian conversational texts"):
  with members:
    - "on the strength of character language models for multilingual named entity recognition"
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"
    - "universal language model fine tuning for text classification"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-7 (induced by source paper "collecting indicators of compromise from unstructured text of cybersecurity articles using neural based sequence labelling"):
  with members:
    - "automatic identification of indicators of compromise using neural based sequence labelling"
    - "deep contextualized word representations"
    - "neural cross lingual named entity recognition with minimal resources"
    - "attention is all you need"
    - "de identification of medical records using conditional random fields and long short term memory networks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "empower sequence labeling with task aware neural language model"
    - "de identification of patient notes with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning of privacy preserving text representations for de identification of medical records"):
  with members:
    - "adversarial removal of demographic attributes from text data"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "de identification of patient notes with recurrent neural networks"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

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)