human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "structure and parameter learning for causal independence and causal interaction models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gaussian process networks")
  Member "stochastic simulation algorithms for dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "factored probabilistic belief tracking")
  Member "a computational scheme for reasoning in dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured arc reversal and simulation of dynamic probabilistic networks")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "learning bayesian networks with local structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "the bayesian structural em algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "logical interpretations of autoencoders")
  Member "learning gaussian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "causal discovery with reinforcement learning")

Question: Please tell me which class the source paper 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)