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 "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"):
  Abstract: Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.

Includes members:
  Member "an algorithm for the construction of bayesian network structures from data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data")
  Member "the recovery of causal poly trees from statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dempster shafer belief function a new interpretation")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms")
  Member "causal inference and causal explanation with background knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cupc cuda based parallel pc algorithm for causal structure learning on gpu")
  Member "a transformational characterization of equivalent bayesian network structures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "causal networks semantics and expressiveness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "learning bayesian network structure from massive datasets the sparse candidate algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sample debiasing in the themis open world database system extended version")

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)