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: "estimating missing data in temporal data streams using multi directional recurrent neural networks"
  Abstract: Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different - and often irregular - times. Accurate estimation of those missing measurements is critical for many reasons, including diagnosis, prognosis and treatment. Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data). We propose a new approach, based on a novel deep learning architecture that we call a Multi-directional Recurrent Neural Network (M-RNN) that interpolates within data streams and imputes across data streams. We demonstrate the power of our approach by applying it to five real-world medical datasets. We show that it provides dramatically improved estimation of missing measurements in comparison to 11 state-of-the-art benchmarks (including Spline and Cubic Interpolations, MICE, MissForest, matrix completion and several RNN methods); typical improvements in Root Mean Square Error are between 35% - 50%. Additional experiments based on the same five datasets demonstrate that the improvements provided by our method are extremely robust.

Connected through hyperedge HE-1 (induced by source paper "uncertainty gated stochastic sequential model for ehr mortality prediction"):
  with members:
    - "temporal clustering invariance in irregular healthcare time series"
    - "on the variance of the adaptive learning rate and beyond"
    - "brits bidirectional recurrent imputation for time series"
    - "handling incomplete heterogeneous data using vaes"
    - "focal loss for dense object detection"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-2 (induced by source paper "naomi non autoregressive multiresolution sequence imputation"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "brits bidirectional recurrent imputation for time series"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "generating multi agent trajectories using programmatic weak supervision"
    - "end to end non autoregressive neural machine translation with connectionist temporal classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "training recurrent neural networks robust to incomplete data application to alzheimer s disease progression modeling"):
  with members:
    - "robust training of recurrent neural networks to handle missing data for disease progression modeling"
    - "lstm a search space odyssey"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "modeling missing data in clinical time series with rnns"
    - "recurrent neural networks for multivariate time series with missing values"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "mixture based multiple imputation model for clinical data with a temporal dimension"):
  with members:
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "efficient em training of gaussian mixtures with missing data"

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)