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 "structure learning in bayesian networks of moderate size by efficient sampling"):
  Abstract: We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior of any feature. We theoretically prove good properties of our estimators and empirically show that our estimators considerably outperform the estimators from the previous state-of-the-art methods.

Includes members:
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "evaluating anytime algorithms for learning optimal bayesian networks":
    also connected through other hyperedges:
      (none)
  Member "data analysis with bayesian networks a bootstrap approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deriving a usage independent software quality metric")
  Member "advances in bayesian network learning using integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integer programming for learning directed acyclic graphs from continuous data")
  Member "bayesian network learning with cutting planes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the seventh answer set programming competition design and results")
  Member "a simple approach for finding the globally optimal bayesian network structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dag gnn dag structure learning with graph neural networks")
  Member "bayesian structure learning using dynamic programming and mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dag gnn dag structure learning with graph neural networks")
  Member "bayesian model averaging using the k best bayesian network structures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding all bayesian network structures within a factor of optimal")

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)