human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "comparing apples and oranges measuring differences between data mining results"):
  Abstract: Deciding whether the results of two different mining algorithms provide significantly different information is an important open problem in exploratory data mining. Whether the goal is to select the most informative result for analysis, or decide which mining approach will likely provide the most novel insight, it is essential that we can tell how different the information is that two results provide. #R##N#In this paper we take a first step towards comparing exploratory results on binary data. We propose to meaningfully convert results into sets of noisy tiles, and compare between these sets by Maximum Entropy modelling and Kullback-Leibler divergence. The measure we construct this way is flexible, and allows us to naturally include background knowledge, such that differences in results can be measured from the perspective of what a user already knows. Furthermore, adding to its interpretability, it coincides with Jaccard dissimilarity when we only consider exact tiles. #R##N#Our approach provides a means to study and tell differences between results of different data mining methods. As an application, we show that it can also be used to identify which parts of results best redescribe other results. Experimental evaluation shows our measure gives meaningful results, correctly identifies methods that are similar in nature, and automatically provides sound redescriptions of results.

Includes members:
  Member "distances between data sets based on summary statistics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "comparing apples and oranges measuring differences between exploratory data mining results")
  Member "comparing apples and oranges measuring differences between exploratory data mining results":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the primping routine tiling through proximal alternating linearized minimization")
  Member "tell me what i need to know succinctly summarizing data with itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "summarizing event sequences with serial episodes a statistical model and an application")
  Member "maximum entropy models and subjective interestingness an application to tiles in binary databases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable dyadic independence models with local and global constraints")
  Member "an approximation ratio for biclustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparing apples and oranges measuring differences between exploratory data mining results")
  Member "computational complexity of queries based on itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interactive and iterative discovery of entity network subgraphs")

Question: Please tell me which class the source paper 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)