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: "an improved admissible heuristic for learning optimal bayesian networks"
  Abstract: Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents independently. As a result, the heuristic may contain many directed cycles and result in a loose bound. This paper introduces an improved admissible heuristic that tries to avoid directed cycles within small groups of variables. A sparse representation is also introduced to store only the unique optimal parent choices. Empirical results show that the new techniques significantly improved the efficiency and scalability of A* and BFBnB on most of datasets tested in this paper.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "advances in bayesian network learning using integer programming"
    - "bayesian network learning with cutting planes"
    - "exact structure discovery in bayesian networks with less space"

Connected through hyperedge HE-3 (induced by source paper "a parallel algorithm for exact bayesian structure discovery in bayesian networks"):
  with members:
    - "computing posterior probabilities of structural features in bayesian networks"
    - "exact structure discovery in bayesian networks with less space"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "evaluating anytime algorithms for learning optimal bayesian networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "theory refinement on bayesian networks"
    - "improving the scalability of optimal bayesian network learning with external memory frontier breadth first branch and bound search"
    - "bayesian network learning with cutting planes"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "anytime heuristic search"

Connected through hyperedge HE-5 (induced by source paper "advances in bayesian network learning using integer programming"):
  with members:
    - "bayesian network learning with cutting planes"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "bayesian network learning by compiling to weighted max sat"

Connected through hyperedge HE-6 (induced by source paper "learning sparse causal models is not np hard"):
  with members:
    - "a logical characterization of constraint based causal discovery"
    - "bayesian network learning with cutting planes"

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)