human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on local optima in learning bayesian networks"
  Abstract: This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported showing that KES often finds a better local optima than GES. Moreover, we use KES to experimentally confirm that the number of different local optima is often huge.

Connected through hyperedge HE-1 (induced by source paper "towards gaussian bayesian network fusion"):
  with members:
    - "aggregating learned probabilistic beliefs"
    - "the topological fusion of bayes nets"
    - "learning gaussian networks"

Connected through hyperedge HE-2 (induced by source paper "large sample learning of bayesian networks is np hard"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "a transformational characterization of equivalent bayesian network structures"
    - "properties of bayesian belief network learning algorithms"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-3 (induced by source paper "learning bayesian network equivalence classes with ant colony optimization"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "conditions under which conditional independence and scoring methods lead to identical selection of bayesian network models"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "theory refinement on bayesian networks"
    - "causal inference in the presence of latent variables and selection bias"
    - "a bayesian network scoring metric that is based on globally uniform parameter priors"

Question: Please tell me which class the center node 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)