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: "exact structure discovery in bayesian networks with less space"
  Abstract: The fastest known exact algorithms for scorebased structure discovery in Bayesian networks on n nodes run in time and space 2nnO(1). The usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement. Here, we study space-time tradeoffs for finding an optimal network structure. When little space is available, we apply the Gurevich-Shelah recurrence-originally proposed for the Hamiltonian path problem-and obtain time 22n-snO(1) in space 2snO(1) for any s = n/2, n/4, n/8, . . .; we assume the indegree of each node is bounded by a constant. For the more practical setting with moderate amounts of space, we present a novel scheme. It yields running time 2n(3/2)pnO(1) in space 2n(3/4)pnO(1) for any p = 0, 1, . . ., n/2; these bounds hold as long as the indegrees are at most 0.238n. Furthermore, the latter scheme allows easy and efficient parallelization beyond previous algorithms. We also explore empirically the potential of the presented techniques.

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"
    - "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"
    - "advances in exact bayesian structure discovery in bayesian networks"

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"
    - "a simple approach for finding the globally optimal bayesian network structure"

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"
    - "an improved admissible heuristic for learning optimal bayesian networks"
    - "advances in exact bayesian structure discovery in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "an improved admissible heuristic for learning optimal bayesian networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "a branch and bound algorithm for mdl learning 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"
    - "additive pattern database heuristics"

Connected through hyperedge HE-5 (induced by source paper "improving the scalability of optimal bayesian network learning with external memory frontier breadth first branch and bound search"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a branch and bound algorithm for mdl learning bayesian networks"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "cached sufficient statistics for efficient machine learning with large datasets"

Connected through hyperedge HE-6 (induced by source paper "exact maximum margin structure learning of bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "theory refinement on bayesian networks"
    - "bayesian network learning with cutting planes"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "maximum margin bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "algorithms and complexity results for exact bayesian structure learning"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "a complete anytime algorithm for treewidth"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "pac learning bounded tree width graphical models"

Connected through hyperedge HE-8 (induced by source paper "on finding optimal polytrees"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "algorithms and complexity results for exact bayesian structure learning"

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)