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 "utility mining across multi sequences with individualized thresholds"):
  Abstract: Utility-oriented pattern mining has become an emerging topic since it can reveal high-utility patterns (e.g., itemsets, rules, sequences) from different types of data, which provides more information than the traditional frequent/confident-based pattern mining models. The utilities of various items are not exactly equal in realistic situations; each item has its own utility or importance. In general, user considers a uniform minimum utility (minutil) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while the minutil is set extremely high or low. We first design a new utility mining framework namely USPT for mining high-Utility Sequential Patterns across multi-sequences with individualized Thresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results showed that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.

Includes members:
  Member "huopm high utility occupancy pattern mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "correlated utility based pattern mining")
  Member "a survey of parallel sequential pattern mining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic modeling for novelty detection with applications to fraud identification")
  Member "privacy preserving utility mining a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "huopm high utility occupancy pattern 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)