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: "stcn stochastic temporal convolutional networks"
  Abstract: Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism. However, currently there remains a performance gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modeling of handwritten text.

Connected through hyperedge HE-1 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "recurrent convolutions for causal 3d cnns"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "convolutional image captioning"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "explainable artificial intelligence model to predict acute critical illness from electronic health records"):
  with members:
    - "deepsofa a continuous acuity score for critically ill patients using clinically interpretable deep learning"
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "early detection of sepsis utilizing deep learning on electronic health record event sequences"
    - "explainable ai for trees from local explanations to global understanding"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "re examination of the role of latent variables in sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixelsnail an improved autoregressive generative model"
    - "sgdr stochastic gradient descent with restarts"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"

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)