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: "rethinking clinical prediction why machine learning must consider year of care and feature aggregation"
  Abstract: Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do not account for dates and limit the generalisability of date-agnostic models. In this work, we establish the magnitude of this problem on MIMIC, a public hospital dataset, and showcase a simple solution. We augment MIMIC with the year in which care was provided and show that a model trained using standard feature representations will significantly degrade in quality over time. We find a deterioration of 0.3 AUC when evaluating mortality prediction on data from 10 years later. We find a similar deterioration of 0.15 AUC for length-of-stay. In contrast, we demonstrate that clinically-oriented aggregates of raw features significantly mitigate future deterioration. Our suggested aggregated representations, when retrained yearly, have prediction quality comparable to year-agnostic models.

Connected through hyperedge HE-1 (induced by source paper "variational resampling based assessment of deep neural networks under distribution shift"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "adversarial invariant feature learning with accuracy constraint for domain generalization"
    - "tutorial and survey on probabilistic graphical model and variational inference in deep reinforcement learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"

Connected through hyperedge HE-2 (induced by source paper "resampling based assessment of robustness to distribution shift for deep neural networks"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "adversarial examples are a natural consequence of test error in noise"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "using pre training can improve model robustness and uncertainty"
    - "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-3 (induced by source paper "reproducibility in machine learning for health"):
  with members:
    - "generalizability of predictive models for intensive care unit patients"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "datasheets for datasets"
    - "scalable and accurate deep learning for electronic health records"
    - "deep reinforcement learning that matters"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-4 (induced by source paper "isthmus secure scalable real time and robust machine learning platform for healthcare"):
  with members:
    - "scalable and accurate deep learning for electronic health records"

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)