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: "end to end learning of lda by mirror descent back propagation over a deep architecture"
  Abstract: We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for maximum a posterior inference and (ii) back propagation over a deep architecture together with stochastic gradient/mirror descent for model parameter estimation, leading to scalable and end-to-end discriminative learning of the model. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model (i.e., BP-LDA). Experimental results on three real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised topic models, neural networks, and is on par with deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "cooperative neural networks conn exploiting prior independence structure for improved classification"):
  with members:
    - "deep image super resolution via natural image priors"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "dynamic routing between capsules"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "discriminative embeddings of latent variable models for structured data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "neural variational inference for text processing"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "prediction focused topic models for electronic health records"):
  with members:
    - "prior matters simple and general methods for evaluating and improving topic quality in topic modeling"
    - "prediction constrained topic models for antidepressant recommendation"
    - "crime topic modeling"
    - "variable selection for latent dirichlet allocation"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-3 (induced by source paper "differentiable greedy networks"):
  with members:
    - "attention is all you need"
    - "maximal sparsity with deep networks"
    - "enriching word vectors with subword information"
    - "rationalizing neural predictions"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "large margin learning of submodular summarization methods"

Connected through hyperedge HE-4 (induced by source paper "prediction constrained topic models for antidepressant recommendation"):
  with members:
    - "spectral learning for supervised topic models"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

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)