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 "discovering descriptive tile trees by mining optimal geometric subtiles"):
  Abstract: When analysing binary data, the ease at which one can interpret results is very important. Many existing methods, however, discover either models that are difficult to read, or return so many results interpretation becomes impossible. Here, we study a fully automated approach for mining easily interpretable models for binary data. We model data hierarchically with noisy tiles-rectangles with significantly different density than their parent tile. To identify good trees, we employ the Minimum Description Length principle. #R##N#We propose STIJL, a greedy any-time algorithm for mining good tile trees from binary data. Iteratively, it finds the locally optimal addition to the current tree, allowing overlap with tiles of the same parent. A major result of this paper is that we find the optimal tile in only $\Theta(NM\min(N, M))$ time. STIJL can either be employed as a top-$k$ miner, or by MDL we can identify the tree that describes the data best. #R##N#Experiments show we find succinct models that accurately summarise the data, and, by their hierarchical property are easily interpretable.

Includes members:
  Member "tell me what i need to know succinctly summarizing data with itemsets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "comparing apples and oranges measuring differences between data mining results")
  Member "are your items in order":
    also connected through other hyperedges:
      (none)
  Member "maximum entropy models and subjective interestingness an application to tiles in binary databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable dyadic independence models with local and global constraints")

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.DS(Data Structures and Algorithms)