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: "interesting multi relational patterns"
  Abstract: Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for highly simplified types of data, such as an attribute-value table or a binary database, such that those methods are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected subgraphs in a representation of the database as a K-partite graph. We show how this pattern syntax is generally applicable to multirelational data, while it reduces to well-known tiles [7] when the data is a simple binary or attribute-value table. We propose RMiner, an efficient algorithm to mine such patterns, and we introduce a method for quantifying their interestingness when contrasted with prior information of the data miner. Finally, we illustrate the usefulness of our approach by discussing results on real-world and synthetic databases.

Connected through hyperedge HE-1 (induced by source paper "interactive and iterative discovery of entity network subgraphs"):
  with members:
    - "examine exploring annotated modules in networks"
    - "comparing apples and oranges measuring differences between exploratory data mining results"
    - "summarizing data succinctly with the most informative itemsets"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-2 (induced by source paper "interactive discovery of coordinated relationship chains with maximum entropy models"):
  with members:
    - "comparing apples and oranges measuring differences between exploratory data mining results"
    - "summarizing data succinctly with the most informative itemsets"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "introduction to machine learning class notes 67577"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-3 (induced by source paper "universal approximation of edge density in large graphs"):
  with members:
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "continuous queries for multi relational graphs"):
  with members:
    - "gsketch on query estimation in graph streams"

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)