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 "mining frequent neighborhood patterns in a large labeled graph"):
  Abstract: Over the years, frequent subgraphs have been an important kind of targeted pattern in pattern mining research, where most approaches deal with databases holding a number of graph transactions, e.g., the chemical structures of compounds. These methods rely heavily on the downward-closure property (DCP) of the support measure to ensure an efficient pruning of the candidate patterns. When switching to the emerging scenario of single-graph databases such as Google's Knowledge Graph and Facebook's social graph, the traditional support measure turns out to be trivial (either 0 or 1). However, to the best of our knowledge, all attempts to redefine a single-graph support have resulted in measures that either lose DCP, or are no longer semantically intuitive. This paper targets pattern mining in the single-graph setting. We propose mining a new class of patterns called frequent neighborhood patterns, which is free from the "DCP-intuitiveness" dilemma of mining frequent subgraphs in a single graph. A neighborhood is a specific topological pattern in which a vertex is embedded, and the pattern is frequent if it is shared by a large portion (above a given threshold) of vertices. We show that the new patterns not only maintain DCP, but also have equally significant interpretations as subgraph patterns. Experiments on real-life datasets support the feasibility of our algorithms on relatively large graphs, as well as the capability of mining interesting knowledge that is not discovered by prior methods.

Includes members:
  Member "meta path based collective classification in heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collective classification of spam campaigners on twitter a hierarchical meta path based approach")
  Member "mining patterns in networks using homomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge representation analysis of graph mining")

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)