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: "on the latent variable interpretation in sum product networks"
  Abstract: One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "advances in machine learning for the behavioral sciences"):
  with members:
    - "using machine learning to guide cognitive modeling a case study in moral reasoning"
    - "wordnet feelings a linguistic categorisation of human feelings"
    - "mlic a maxsat based framework for learning interpretable classification rules"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "semantics derived automatically from language corpora contain human like biases"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep learning in neural networks"
    - "parliamentary voting procedures agenda control manipulation and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "counterfactuals uncover the modular structure of deep generative models"
    - "on relaxing determinism in arithmetic circuits"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "budgeted experiment design for causal structure learning"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "on the relationship between sum product networks and bayesian networks"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "visualizing and understanding sum product networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "learning tractable probabilistic models for fault localization"
    - "understanding neural networks through deep visualization"
    - "made masked autoencoder for distribution estimation"
    - "on the relationship between sum product networks and bayesian networks"
    - "visualizing and understanding recurrent networks"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-4 (induced by source paper "conditional sum product networks imposing structure on deep probabilistic architectures"):
  with members:
    - "visualizing and understanding sum product networks"
    - "learning logistic circuits"
    - "probabilistic deep learning using random sum product networks"
    - "on the relative expressiveness of bayesian and neural networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "on relaxing determinism in arithmetic circuits"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "sum product quotient networks"

Connected through hyperedge HE-5 (induced by source paper "smoothing structured decomposable circuits"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "the sum product theorem a foundation for learning tractable models"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "expander cnfs have exponential dnnf size"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-6 (induced by source paper "bayesian learning of sum product networks"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "probabilistic deep learning using random sum product networks"
    - "tensorial mixture models"
    - "a unified approach for learning the parameters of sum product networks"
    - "neural architecture search with reinforcement learning"
    - "on the relationship between sum product networks and bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-7 (induced by source paper "optimisation of overparametrized sum product networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "probabilistic deep learning using random sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "learning deep generative spatial models for mobile robots"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-8 (induced by source paper "deep structured mixtures of gaussian processes"):
  with members:
    - "bayesian learning of sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"
    - "sum product networks a new deep architecture"

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.AI(Artificial Intelligence)