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: "phenotyping of clinical time series with lstm recurrent neural networks"
  Abstract: We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics.

Connected through hyperedge HE-1 (induced by source paper "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"):
  with members:
    - "exploring dimensionality reductions with forward and backward projections"
    - "brain tumor segmentation with deep neural networks"
    - "understanding hidden memories of recurrent neural networks"
    - "direct manipulation visualization of deep networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "machine learning and visualization in clinical decision support current state and future directions"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "spartan scalable parafac2 for large sparse data"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "improving palliative care with deep learning"
    - "the use of autoencoders for discovering patient phenotypes"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "medical diagnosis from laboratory tests by combining generative and discriminative learning"):
  with members:
    - "modeling missing data in clinical time series with rnns"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "recurrent neural networks for multivariate time series with missing values"
    - "a recurrent latent variable model for sequential data"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)