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 exploratory data mining results"):
  Abstract: Deciding whether the results of two different mining algorithms provide significantly different information is an important, yet understudied, open problem in exploratory data mining. Whether the goal is to select the most informative result for analysis, or to decide which mining approach will most likely provide the most novel insight, it is essential that we can tell how different the information is that different results by possibly different methods provide. In this paper we take a first step towards comparing exploratory data mining 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, well-founded notions from Information Theory. We so construct a measure that is highly 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. Our approach provides a means to study and tell differences between results of different exploratory data mining methods. As an application, we show that our measure can also be used to identify which parts of results best redescribe other results. Furthermore, we study its use for iterative data mining, where one iteratively wants to find that result that will provide maximal novel information. Experimental evaluation shows our measure gives meaningful results, correctly identifies methods that are similar in nature, automatically provides sound redescriptions of results, and is highly applicable for iterative data mining.

Includes members:
  Member "comparing apples and oranges measuring differences between data mining results":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating realistic synthetic population datasets")
  Member "distances between data sets based on summary statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "comparing apples and oranges measuring differences between data mining results")
  Member "summarizing data succinctly with the most informative itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining insights from weakly structured event data")
  Member "tell me what i need to know succinctly summarizing data with itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comparing apples and oranges measuring differences between data mining results")
  Member "maximum entropy models and subjective interestingness an application to tiles in binary databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "comparing apples and oranges measuring differences between data mining results")
  Member "computational complexity of queries based on itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "comparing apples and oranges measuring differences between data mining results")

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)