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 "finding all bayesian network structures within a factor of optimal"):
  Abstract: A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches.

Includes members:
  Member "entropy based pruning for learning bayesian networks using bic":
    also connected through other hyperedges:
      (none)
  Member "tractable bayesian learning of tree belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning selectively conditioned forest structures with applications to dbns and classification")
  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 "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 "a simple approach for finding the globally optimal bayesian network structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "budgeted experiment design for causal structure learning")
  Member "ordering based search a simple and effective algorithm for learning bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "characterizing distribution equivalence for cyclic and acyclic directed graphs")

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)