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: "properties of bayesian belief network learning algorithms"
  Abstract: Bayesian belief network learning algorithms have three basic components: a measure of a network structure and a database, a search heuristic that chooses network structures to be considered, and a method of estimating the probability tables from the database. This paper contributes to all these three topics. The behavior of the Bayesian measure of Cooper and Herskovits and a minimum description length (MDL) measure are compared with respect to their properties for both limiting size and finite size databases. It is shown that the MDL measure has more desirable properties than the Bayesian measure when a distribution is to be learned. It is shown that selecting belief networks with certain minimallity properties is NP-hard. This result justifies the use of search heuristics instead of exact algorithms for choosing network structures to be considered. In some cases, a collection of belief networks can be represented by a single belief network which leads to a new kind of probability table estimation called smoothing. We argue that smoothing can be efficiently implemented by incorporating it in the search heuristic. Experimental results suggest that for learning probabilities of belief networks smoothing is helpful.

Connected through hyperedge HE-1 (induced by source paper "inferring coupling of distributed dynamical systems via transfer entropy"):
  with members:
    - "learning the structure of dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-2 (induced by source paper "learning the structure of nonlinear dynamical networks an information theoretic perspective"):
  with members:
    - "learning the structure of dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-3 (induced by source paper "a structurally and temporally extended bayesian belief network model definitions properties and modeling techniques"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "probabilistic temporal reasoning with endogenous change"
    - "forecasting sleep apnea with dynamic network models"

Connected through hyperedge HE-4 (induced by source paper "a branch and bound algorithm for mdl learning bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-5 (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"
    - "on local optima in learning bayesian networks"
    - "finding a path is harder than finding a tree"

Connected through hyperedge HE-6 (induced by source paper "probabilistic inference from arbitrary uncertainty using mixtures of factorized generalized gaussians"):
  with members:
    - "active learning with statistical models"

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.AI(Artificial Intelligence)