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 "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"):
  Abstract: We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.

Includes members:
  Member "exploring dimensionality reductions with forward and backward projections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a visual interaction framework for dimensionality reduction based data exploration")
  Member "brain tumor segmentation with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "understanding hidden memories of recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "direct manipulation visualization of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "medgan medical image translation using gans")
  Member "visualizing deep neural network decisions prediction difference analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "towards better analysis of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")

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)