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: "directly modeling missing data in sequences with rnns improved classification of clinical time series"
  Abstract: We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequences. In this work, we show the remarkable ability of RNNs to make effective use of binary indicators to directly model missing data, improving AUC and F1 significantly. However, while RNNs can learn arbitrary functions of the missing data and observations, linear models can only learn substitution values. For linear models and MLPs, we show an alternative strategy to capture this signal. Additionally, we evaluate LSTMs, MLPs, and linear models trained on missingness patterns only, showing that for several diseases, what tests are run can be more predictive than the results themselves.

Connected through hyperedge HE-1 (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-2 (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"
    - "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 "progressive growing of neural odes"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "interpolation prediction networks for irregularly sampled time series"
    - "neural ordinary differential equations"
    - "teacher student curriculum learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "learning to execute"

Connected through hyperedge HE-4 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "learning temporal point processes via reinforcement learning"
    - "neural decomposition of time series data for effective generalization"
    - "can recurrent neural networks warp time"
    - "deep reinforcement learning of marked temporal point processes"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"

Connected through hyperedge HE-5 (induced by source paper "explaining an increase in predicted risk for clinical alerts"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "dice in the black box user experiences with an inscrutable algorithm"
    - "did the model understand the question"
    - "scalable and accurate deep learning for electronic health records"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "smoothgrad removing noise by adding noise"
    - "clinical intervention prediction and understanding using deep networks"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-6 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series"):
  with members:
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "latent odes for irregularly sampled time series"
    - "deep ensemble tensor factorization for longitudinal patient trajectories classification"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "scalable and accurate deep learning for electronic health records"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "minimal gated unit for recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "mgp atttcn an interpretable machine learning model for the prediction of sepsis"):
  with members:
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "medical time series classification with hierarchical attention based temporal convolutional networks a case study of myotonic dystrophy diagnosis"
    - "model based reinforcement learning for sepsis treatment"
    - "mattnet modular attention network for referring expression comprehension"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "som vae interpretable discrete representation learning on time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"

Connected through hyperedge HE-8 (induced by source paper "set functions for time series"):
  with members:
    - "interpolation prediction networks for irregularly sampled time series"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "conditional neural processes"
    - "deep sets"
    - "attention is all you need"
    - "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "the neural hawkes process a neurally self modulating multivariate point process"

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)