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: "brits bidirectional recurrent imputation for time series"
  Abstract: Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing value imputation in time series data. Our proposed method directly learns the missing values in a bidirectional recurrent dynamical system, without any specific assumption. The imputed values are treated as variables of RNN graph and can be effectively updated during the backpropagation.BRITS has three advantages: (a) it can handle multiple correlated missing values in time series; (b) it generalizes to time series with nonlinear dynamics underlying; (c) it provides a data-driven imputation procedure and applies to general settings with missing data.We evaluate our model on three real-world datasets, including an air quality dataset, a health-care data, and a localization data for human activity. Experiments show that our model outperforms the state-of-the-art methods in both imputation and classification/regression accuracies.

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"
    - "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"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-2 (induced by source paper "uncertainty aware variational recurrent imputation network for clinical time series"):
  with members:
    - "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"
    - "recurrent neural networks for multivariate time series with missing values"
    - "a recurrent latent variable model for sequential data"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (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"
    - "dropblock a regularization method for convolutional networks"
    - "deep sets"
    - "self normalizing neural networks"
    - "mida multiple imputation using denoising autoencoders"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "naomi non autoregressive multiresolution sequence imputation"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "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"
    - "non autoregressive neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values"):
  with members:
    - "megan a generative adversarial network for multi view network embedding"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "a memory network based solution for multivariate time series forecasting"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "end to end learning for short text expansion"

Connected through hyperedge HE-6 (induced by source paper "cdsa cross dimensional self attention for multivariate geo tagged time series imputation"):
  with members:
    - "dynamic spatio temporal graph based cnns for traffic prediction"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "recover missing sensor data with iterative imputing network"
    - "recurrent neural networks for multivariate time series with missing values"
    - "deep residual learning for image recognition"
    - "stacked attention networks for image question answering"

Connected through hyperedge HE-7 (induced by source paper "one dimensional deep image prior for time series inverse problems"):
  with members:
    - "regularization by architecture a deep prior approach for inverse problems"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "random mesh projectors for inverse problems"
    - "phase retrieval under a generative prior"
    - "image restoration using total variation regularized deep image prior"
    - "deep image prior"
    - "recurrent neural networks for multivariate time series with missing values"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "latent odes for irregularly sampled time series"):
  with members:
    - "interpolation prediction networks for irregularly sampled time series"
    - "deepmind control suite"
    - "discrete event continuous time rnns"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "sequence to sequence learning with neural networks"

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)