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 "representation learning for electronic health records"):
  Abstract: Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning. Learning better representations is critical to improve the performance of downstream tasks. Due to the advances in machine learning, we now can learn better and meaningful representations from EHR through disentangling the underlying factors inside data and distilling large amounts of information and knowledge from heterogeneous EHR sources. In this chapter, we first introduce the background of learning representations and reasons why we need good EHR representations in machine learning for medicine and healthcare in Section 1. Next, we explain the commonly-used machine learning and evaluation methods for representation learning using a deep learning approach in Section 2. Following that, we review recent related studies of learning patient state representation from EHR for clinical machine learning tasks in Section 3. Finally, in Section 4 we discuss more techniques, studies, and challenges for learning natural language representations when free texts, such as clinical notes, examination reports, or biomedical literature are used. We also discuss challenges and opportunities in these rapidly growing research fields.

Includes members:
  Member "biobert a pre trained biomedical language representation model for biomedical text mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "multimodal multitask representation learning for pathology biobank metadata prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning for clinical predictive analytics")
  Member "enhancing clinical concept extraction with contextual embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated pretraining and fine tuning of bert using clinical notes from multiple silos")
  Member "clinicalbert modeling clinical notes and predicting hospital readmission":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "clinical concept extraction for document level coding":
    also connected through other hyperedges:
      (none)
  Member "graph convolutional transformer learning the graphical structure of electronic health records":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "unsupervised clinical language translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating classical chinese poems from vernacular chinese")
  Member "publicly available clinical bert embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")

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.LG(Machine Learning)