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: "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
  Abstract: Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research. We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records. We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction. As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system. We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-of-domain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall. Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.

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"
    - "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"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"

Connected through hyperedge HE-3 (induced by source paper "hare a flexible highlighting annotator for ranking and exploration"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "publicly available clinical bert embeddings"
    - "classifying the reported ability in clinical mobility descriptions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "classifying the reported ability in clinical mobility descriptions"):
  with members:
    - "lessons from natural language inference in the clinical domain"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"
    - "enriching word vectors with subword information"
    - "character level convolutional networks for text classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

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)