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: "learning the structure of dynamic probabilistic networks"
  Abstract: Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains.

Connected through hyperedge HE-1 (induced by source paper "on open universe causal reasoning"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "on the conditional logic of simulation models"
    - "building machines that learn and think like people"
    - "axiomatizing causal reasoning"
    - "church a language for generative models"
    - "nonparametric bayesian logic"
    - "towards common sense reasoning via conditional simulation legacies of turing in artificial intelligence"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "ask less scale market research without annoying your customers"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-3 (induced by source paper "basic tasks of sentiment analysis"):
  with members:
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "natural language processing almost from scratch"
    - "learning document level semantic properties from free text annotations"
    - "modeling online reviews with multi grain topic models"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-4 (induced by source paper "dynamic probabilistic network based human action recognition"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "multimodal affect analysis for product feedback assessment"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-5 (induced by source paper "inferring coupling of distributed dynamical systems via transfer entropy"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "properties of bayesian belief network learning algorithms"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-6 (induced by source paper "learning the structure of nonlinear dynamical networks an information theoretic perspective"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "properties of bayesian belief network learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "dynamic sum product networks for tractable inference on sequence data extended version"):
  with members:
    - "on the relationship between sum product networks and bayesian networks"
    - "sequence to sequence learning with neural networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a differential approach to inference in bayesian networks"
    - "tractable inference for complex stochastic processes"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-8 (induced by source paper "discovering the hidden structure of complex dynamic systems"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "tractable inference for complex stochastic processes"
    - "the bayesian structural em algorithm"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "forecasting sleep apnea with dynamic network 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.AI(Artificial Intelligence)