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: "recurrent neural networks for multivariate time series with missing values"
  Abstract: Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provides useful insights for better understanding and utilization of missing values in time series analysis.

Connected through hyperedge HE-1 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery"):
  with members:
    - "tackling climate change with machine learning"
    - "learning parallax attention for stereo image super resolution"
    - "frame and feature context video super resolution"
    - "deep learning for image super resolution a survey"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "learning disentangled representations of satellite image time series"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "mapping informal settlements in developing countries with multi resolution multi spectral data"

Connected through hyperedge HE-2 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data"):
  with members:
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "not to cry wolf distantly supervised multitask learning in critical care"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "a unified approach to interpreting model predictions"
    - "multi task prediction of disease onsets from longitudinal lab tests"

Connected through hyperedge HE-3 (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"
    - "estimating missing data in temporal data streams using multi directional recurrent neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "adaptive prediction timing for electronic health records"):
  with members:
    - "learning hierarchical representations of electronic health records for clinical outcome prediction"
    - "analyzing the role of model uncertainty for electronic health records"
    - "dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre processing"
    - "interpolation prediction networks for irregularly sampled time series"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "scalable and accurate deep learning for electronic health records"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "time series forecasting using lstm networks a symbolic approach"):
  with members:
    - "lstm fully convolutional networks for time series classification"
    - "lstm a search space odyssey"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "uncertainty aware variational recurrent imputation network for clinical time series"):
  with members:
    - "brits bidirectional recurrent imputation for time series"
    - "handling incomplete heterogeneous data using vaes"
    - "methods for interpreting and understanding deep neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "a recurrent latent variable model for sequential data"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "why not to use zero imputation correcting sparsity bias in training neural networks"):
  with members:
    - "misgan learning from incomplete data with generative adversarial networks"
    - "gain missing data imputation using generative adversarial nets"
    - "brits bidirectional recurrent imputation for time series"
    - "dropblock a regularization method for convolutional networks"
    - "deep sets"
    - "self normalizing neural networks"
    - "mida multiple imputation using denoising autoencoders"
    - "neural collaborative filtering"

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)