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: "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
  Abstract: A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper-parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with shifted distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results show that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of dominated hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candidates.

Connected through hyperedge HE-1 (induced by source paper "tutorial and survey on probabilistic graphical model and variational inference in deep reinforcement learning"):
  with members:
    - "maximum entropy regularized multi goal reinforcement learning"
    - "variational resampling based assessment of deep neural networks under distribution shift"
    - "resampling based assessment of robustness to distribution shift for deep neural networks"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

Connected through hyperedge HE-2 (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"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "rethinking clinical prediction why machine learning must consider year of care and feature aggregation"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"

Connected through hyperedge HE-3 (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"
    - "rethinking clinical prediction why machine learning must consider year of care and feature aggregation"
    - "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"

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)