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: "interpretable outcome prediction with sparse bayesian neural networks in intensive care"
  Abstract: Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent years, machine learning tools have been developed to aid this process. Intensive care unit (ICU) admissions represent the most data dense and time-critical patient care episodes. In this context, prediction models may help clinicians determine which patients are most at risk and prioritize care. However, flexible tools such as artificial neural networks (ANNs) suffer from a lack of interpretability limiting their acceptability to clinicians. In this work, we propose a novel interpretable Bayesian neural network architecture which offers both the flexibility of ANNs and interpretability in terms of feature selection. In particular, we employ a sparsity inducing prior distribution in a tied manner to learn which features are important for outcome prediction. We evaluate our approach on the task of mortality prediction using two real-world ICU cohorts. In collaboration with clinicians we found that, in addition to the predicted outcome results, our approach can provide novel insights into the importance of different clinical measurements. This suggests that our model can support medical experts in their decision making process.

Connected through hyperedge HE-1 (induced by source paper "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"):
  with members:
    - "active feature acquisition with supervised matrix completion"
    - "handling incomplete heterogeneous data using vaes"
    - "eddi efficient dynamic discovery of high value information with partial vae"
    - "generative adversarial active learning"
    - "advances in variational inference"
    - "deep bayesian active learning with image data"
    - "matrix completion with queries"
    - "a utility theoretic approach to privacy in online services"

Connected through hyperedge HE-2 (induced by source paper "preserving patient privacy while training a predictive model of in hospital mortality"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed machine learning for on device intelligence"
    - "federated optimization distributed optimization beyond the datacenter"
    - "learning to diagnose with lstm recurrent neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "making bayesian predictive models interpretable a decision theoretic approach"):
  with members:
    - "the challenge of crafting intelligible intelligence"
    - "techniques for interpretable machine learning"
    - "local interpretable model agnostic explanations of bayesian predictive models via kullback leibler projections"
    - "a unified approach to interpreting model predictions"
    - "scalable bayesian rule lists"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "impact of novel aggregation methods for flexible time sensitive ehr prediction without variable selection or cleaning"):
  with members:
    - "dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre processing"
    - "scalable and accurate deep learning for electronic health records"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)