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: "improving the scalability of optimal bayesian network learning with external memory frontier breadth first branch and bound search"
  Abstract: Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memory-efficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadth-first branch and bound search that leverages the layered structure of the search graph of this problem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithm stores most of the graph in external memory, such as hard disk, when it does not fit in RAM. Experimental results show that the resulting algorithm solves significantly larger problems than the current state of the art.

Connected through hyperedge HE-1 (induced by source paper "a new approach for a better load balancing and a better distribution of resources in cloud computing"):
  with members:
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-2 (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"
    - "bayesian network learning with cutting planes"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "an improved admissible heuristic for learning optimal bayesian networks"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "anytime heuristic search"

Connected through hyperedge HE-3 (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"
    - "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"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "additive pattern database heuristics"

Connected through hyperedge HE-4 (induced by source paper "local structure discovery in bayesian networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "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"

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)