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: "probably the best itemsets"
  Abstract: One of the main current challenges in itemset mining is to discover a small set of high-quality itemsets. In this paper we propose a new and general approach for measuring the quality of itemsets. The method is solidly founded in Bayesian statistics and decreases monotonically, allowing for efficient discovery of all interesting itemsets. The measure is defined by connecting statistical models and collections of itemsets. This allows us to score individual itemsets with the probability of them occuring in random models built on the data.   As a concrete example of this framework we use exponential models. This class of models possesses many desirable properties. Most importantly, Occam's razor in Bayesian model selection provides a defence for the pattern explosion. As general exponential models are infeasible in practice, we use decomposable models; a large sub-class for which the measure is solvable. For the actual computation of the score we sample models from the posterior distribution using an MCMC approach.   Experimentation on our method demonstrates the measure works in practice and results in interpretable and insightful itemsets for both synthetic and real-world data.

Connected through hyperedge HE-1 (induced by source paper "summarizing data succinctly with the most informative itemsets"):
  with members:
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "using background knowledge to rank itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "maximum entropy based significance of itemsets"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-2 (induced by source paper "tell me what i need to know succinctly summarizing data with itemsets"):
  with members:
    - "using background knowledge to rank itemsets"
    - "maximum entropy based significance of itemsets"
    - "computational complexity of queries based on itemsets"

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.DB(Databases)