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: "advances in bayesian network learning using integer programming"
  Abstract: We consider the problem of learning Bayesian networks (BNs) from complete discrete data. This problem of discrete optimisation is formulated as an integer program (IP). We describe the various steps we have taken to allow efficient solving of this IP. These are (i) efficient search for cutting planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal) BNs and (iii) tightening the linear relaxation of the IP. After relating this BN learning problem to set covering and the multidimensional 0-1 knapsack problem, we present our empirical results. These show improvements, sometimes dramatic, over earlier results.

Connected through hyperedge HE-1 (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"
    - "bayesian network learning with cutting planes"
    - "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-2 (induced by source paper "finding all bayesian network structures within a factor of optimal"):
  with members:
    - "entropy based pruning for learning bayesian networks using bic"
    - "tractable bayesian learning of tree belief networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian model averaging using the k best bayesian network structures"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-3 (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"
    - "bayesian network learning with cutting planes"
    - "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-4 (induced by source paper "advances in learning bayesian networks of bounded treewidth"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "the bayesian structural em algorithm"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-5 (induced by source paper "learning chordal markov networks by constraint satisfaction"):
  with members:
    - "optimal junction trees"
    - "bayesian network learning with cutting planes"
    - "optimization in smt with la q cost functions"
    - "bayesian network learning by compiling to weighted max sat"

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)