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: "de identification of patient notes with recurrent neural networks"
  Abstract: Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information (PHI) that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of EHR databases, the limited number of researchers with access to the non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. #R##N#Materials and Methods: We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset. #R##N#Results: Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall 97.38 and a precision of 97.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall 99.25 and a precision of 99.06. #R##N#Conclusion: Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no feature engineering.

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 "sensitive data detection and classification in spanish clinical text experiments with bert"):
  with members:
    - "passage re ranking with bert"
    - "unsupervised cross lingual representation learning at scale"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"

Connected through hyperedge HE-3 (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"
    - "transfer learning for named entity recognition with neural networks"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-4 (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"
    - "transfer learning for named entity recognition with neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "named entity recognition for novel types by transfer learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-5 (induced by source paper "towards automatic generation of shareable synthetic clinical notes using neural language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep ehr chronic disease prediction using medical notes"
    - "towards the creation of a large corpus of synthetically identified clinical notes"
    - "fine tuned language models for text classification"
    - "lessons from natural language inference in the clinical domain"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "towards measuring membership privacy"
    - "privacy risk in machine learning analyzing the connection to overfitting"

Connected through hyperedge HE-6 (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"
    - "transfer learning for named entity recognition with neural networks"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-7 (induced by source paper "machine learning for clinical predictive analytics"):
  with members:
    - "multimodal multitask representation learning for pathology biobank metadata prediction"
    - "unsupervised clinical language translation"
    - "towards unsupervised speech to text translation"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "mapping unparalleled clinical professional and consumer languages with embedding alignment"
    - "opportunities in machine learning for healthcare"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"

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"
    - "transfer learning for named entity recognition with neural networks"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "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)