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 "medgraph structural and temporal representation learning of electronic medical records"):
  Abstract: Electronic medical record (EMR) data contains historical sequences of visits of patients, and each visit contains rich information, such as patient demographics, hospital utilisation and medical codes, including diagnosis, procedure and medication codes.  Most existing EMR embedding methods capture visit-code associations by constructing input visit representations as binary vectors with a static vocabulary of medical codes.  With this limited representation, they fail in encapsulating rich attribute information of visits (demographics and utilisation information) and/or codes (e.g., medical code descriptions).  Furthermore, current work considers visits of the same patient as discrete-time events and ignores time gaps between them.  However, the time gaps between visits depict dynamics of the patient's medical history inducing varying influences on future visits.  To address these limitations, we present $\mathtt{MedGraph}$, a supervised EMR embedding method that captures two types of information: (1) the visit-code associations in an attributed bipartite graph, and (2) the temporal sequencing of visits through a point process.  $\mathtt{MedGraph}$ produces Gaussian embeddings for visits and codes to model the uncertainty.  We evaluate the performance of $\mathtt{MedGraph}$ through an extensive experimental study and show that $\mathtt{MedGraph}$ outperforms state-of-the-art EMR embedding methods in several medical risk prediction tasks.

Includes members:
  Member "gaussian embedding of large scale attributed graphs":
    also connected through other hyperedges:
      (none)
  Member "learning the graphical structure of electronic health records with graph convolutional transformer":
    also connected through other hyperedges:
      (none)
  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-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "medical concept embedding with time aware attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "mime multilevel medical embedding of electronic health records for predictive healthcare":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepenroll patient trial matching with deep embedding and entailment prediction")
  Member "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)