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: "a deep learning architecture for de identification of patient notes implementation and evaluation"
  Abstract: De-identification is the process of removing 18 protected health information (PHI) from clinical notes in order for the text to be considered not individually identifiable. Recent advances in natural language processing (NLP) has allowed for the use of deep learning techniques for the task of de-identification. In this paper, we present a deep learning architecture that builds on the latest NLP advances by incorporating deep contextualized word embeddings and variational drop out Bi-LSTMs. We test this architecture on two gold standard datasets and show that the architecture achieves state-of-the-art performance on both data sets while also converging faster than other systems without the use of dictionaries or other knowledge sources.

Connected through hyperedge HE-1 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "comparing rule based feature based and deep neural methods for de identification of dutch medical records"):
  with members:
    - "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"
    - "de identification of patient notes with recurrent neural networks"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-3 (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-4 (induced by source paper "publicly available clinical bert embeddings"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "clinical concept extraction with contextual word embedding"

Connected through hyperedge HE-5 (induced by source paper "scrubbing sensitive phi data from medical records made easy by spacy a scalable model implementation comparisons"):
  with members:
    - "phi scrubber a deep learning approach"
    - "robust lexical features for improved neural network named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"

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)