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 "advances in learning bayesian networks of bounded treewidth"):
  Abstract: This work presents novel algorithms for learning Bayesian network structures with bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed-integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in uniformly sampling $k$-trees (maximal graphs of treewidth $k$), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that $k$-tree. Some properties of these methods are discussed and proven. The approaches are empirically compared to each other and to a state-of-the-art method for learning bounded treewidth structures on a collection of public data sets with up to 100 variables. The experiments show that our exact algorithm outperforms the state of the art, and that the approximate approach is fairly accurate.

Includes members:
  Member "maximum likelihood bounded tree width markov networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "taming the curse of dimensionality discrete integration by hashing and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient discrete integration via adaptive quantile queries")
  Member "the bayesian structural em algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "logical interpretations of autoencoders")
  Member "learning bayesian network structure from massive datasets the sparse candidate algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sample debiasing in the themis open world database system extended version")
  Member "advances in bayesian network learning using integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "the seventh answer set programming competition design and results")
  Member "exact structure discovery in bayesian networks with less space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "integer programming for learning directed acyclic graphs from continuous data")

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)