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 "hybrid asp based approach to pattern mining"):
  Abstract: Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. #R##N#Under consideration in Theory and Practice of Logic Programming (TPLP).

Includes members:
  Member "reductions for frequency based data mining problems":
    also connected through other hyperedges:
      (none)
  Member "knowledge representation analysis of graph mining":
    also connected through other hyperedges:
      (none)
  Member "an efficient algorithm for mining frequent sequence with constraint programming":
    also connected through other hyperedges:
      (none)
  Member "graph isomorphism in quasipolynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")
  Member "constraint based sequence mining using constraint programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "negpspan efficient extraction of negative sequential patterns with embedding constraints")
  Member "using answer set programming for pattern mining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficiency analysis of asp encodings for sequential pattern mining tasks")
  Member "a constraint programming approach for mining sequential patterns in a sequence database":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficiency analysis of asp encodings for sequential pattern mining tasks")
  Member "design and implementation of aggregate functions in the dlv system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shared aggregate sets in answer set programming")

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.AI(Artificial Intelligence)