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: "bayesian network learning with cutting planes"
  Abstract: The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisation problem where the goal is to find a BN structure which maximises log marginal likelihood (BDe score). Integer programming, specifically the SCIP framework, is used to solve this optimisation problem. Acyclicity constraints are added to the integer program (IP) during solving in the form of cutting planes. Finding good cutting planes is the key to the success of the approach -the search for such cutting planes is effected using a sub-IP. Results show that this is a particularly fast method for exact BN learning.

Connected through hyperedge HE-1 (induced by source paper "the seventh answer set programming competition design and results"):
  with members:
    - "efficiently coupling the i dlv grounder with asp solvers"
    - "optimizing answer set computation via heuristic based decomposition"
    - "on the computation of paracoherent answer sets"
    - "casp solutions for planning in hybrid domains"
    - "stable unstable semantics beyond np with normal logic programs"
    - "asperix a first order forward chaining approach for answer set computing"
    - "optimizing phylogenetic supertrees using answer set programming"
    - "predicate logic as a modeling language modeling and solving some machine learning and data mining problems with idp3"

Connected through hyperedge HE-2 (induced by source paper "boosting answer set optimization with weighted comparator networks"):
  with members:
    - "a new look at bdds for pseudo boolean constraints"
    - "extended asp tableaux and rule redundancy in normal logic programs1"

Connected through hyperedge HE-3 (induced by source paper "integer programming for learning directed acyclic graphs from continuous data"):
  with members:
    - "information theoretic limits of bayesian network structure learning"
    - "bayesian network structure learning with integer programming polytopes facets and complexity"
    - "advances in bayesian network learning using integer programming"
    - "exact structure discovery in bayesian networks with less space"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "gradient based neural dag learning"):
  with members:
    - "dag gnn dag structure learning with graph neural networks"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-5 (induced by source paper "efficient learning of bounded treewidth bayesian networks from complete and incomplete data sets"):
  with members:
    - "learning bayesian networks with local structure"
    - "the bayesian structural em algorithm"
    - "join graph propagation algorithms"

Connected through hyperedge HE-6 (induced by source paper "bayesian network structure learning with integer programming polytopes facets and complexity"):
  with members:
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "exact maximum margin structure learning of bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "structure learning in bayesian networks of moderate size by efficient sampling"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "evaluating anytime algorithms for learning optimal bayesian networks"
    - "data analysis with bayesian networks a bootstrap approach"
    - "advances in bayesian network learning using integer programming"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian structure learning using dynamic programming and mcmc"
    - "bayesian model averaging using the k best bayesian network structures"
    - "partial order mcmc for structure discovery in bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "incorporating type ii error probabilities from independence tests into score based learning of bayesian network structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "on the sample complexity of learning bayesian networks"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "strong faithfulness and uniform consistency in causal inference"
    - "on the number of samples needed to learn the correct structure of a bayesian network"

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)