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 "incorporating type ii error probabilities from independence tests into score based learning of bayesian network structure"):
  Abstract: We give a new consistent scoring function for struc- ture learning of Bayesian networks. In contrast to traditional ap- proaches to score-based structure learning, such as BDeu or MDL, the complexity penalty that we propose is data-dependent and is given by the probability that a conditional independence test correctly shows that an edge cannot exist. What really distin- guishes this new scoring function from earlier work is that it has the property of becoming computationally easier to maximize as the amount of data increases. We prove a polynomial sample com- plexity result, showing that maximizing this score is guaranteed to correctly learn a structure with no false edges and a distribu- tion close to the generating distribution, whenever there exists a Bayesian network which is a perfect map for the data generat- ing distribution. Although the new score can be used with any search algorithm, in (BS13) we have given empirical results show- ing that it is particularly eective when used together with a linear programming relaxation approach to Bayesian network structure learning. The present paper contains all details of the proofs of the nite-sample complexity results in ( BS13) as well as detailed explanation of the computation of the certain error probabilities called -values, whose precomputation and tabulation is necessary for the implementation of the algorithm in (BS13).

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 "on the sample complexity of learning bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "testing bayesian networks")
  Member "learning bayesian network structure from massive datasets the sparse candidate algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sample debiasing in the themis open world database system extended version")
  Member "sparsityboost a new scoring function for learning bayesian network structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computationally and statistically efficient learning of causal bayes nets using path queries")
  Member "bayesian network learning with cutting planes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the seventh answer set programming competition design and results")
  Member "strong faithfulness and uniform consistency in causal inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning directed graphical models from gaussian data")
  Member "on the number of samples needed to learn the correct structure of a bayesian network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "survey of bayesian networks applications on unmanned intelligent autonomous vehicles")

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.LG(Machine Learning)