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: "bayesian models of data streams with hierarchical power priors"
  Abstract: Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-conjugate hierarchical priors to explicitly model temporal changes of the model parameters. We also derive a novel variational inference scheme which overcomes the use of non-conjugate priors while maintaining the computational efficiency of variational methods over conjugate models. The approach is validated on three real data sets over three latent variable models.

Connected through hyperedge HE-1 (induced by source paper "dynamic transformation of prior knowledge intobayesian models for data streams"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "improving topic models with latent feature word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "session based recommendations with recurrent neural networks"
    - "dynamic poisson factorization"
    - "distributed representations of words and phrases and their compositionality"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-2 (induced by source paper "graph convolutional topic model for data streams"):
  with members:
    - "graph convolutional networks for text classification"
    - "memory aware synapses learning what not to forget"
    - "continual learning through synaptic intelligence"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "overcoming catastrophic forgetting in neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "probabilistic models with deep neural networks"):
  with members:
    - "inferpy probabilistic modeling with deep neural networks made easy"
    - "pyro deep universal probabilistic programming"
    - "implicit reparameterization gradients"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "dice the infinitely differentiable monte carlo estimator"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"

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)