human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  Abstract: Abstract   Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture prior knowledge which can transferred to downstream tasks. Even though embeddings have become de facto standard for text representation in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance research in clinical embeddings.

Includes members:
  Member "classifying medical relations in clinical text via convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reflex flexible framework for relation extraction in multiple domains")
  Member "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "explainable prediction of medical codes from clinical text":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scopeit scoping task relevant sentences in documents")
  Member "embedding transfer for low resource medical named entity recognition a case study on patient mobility":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes")
  Member "medical concept embedding with time aware attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal self attention network for medical concept embedding")
  Member "patient representation learning and interpretable evaluation using clinical notes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards unstructured mortality prediction with free text clinical notes")
  Member "semantic structure and interpretability of word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings")
  Member "improving medical short text classification with semantic expansion using word cluster embedding":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)