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: "privacy preserving utility mining a survey"
  Abstract: In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.

Connected through hyperedge HE-1 (induced by source paper "huopm high utility occupancy pattern mining"):
  with members:
    - "a survey of utility oriented pattern mining"

Connected through hyperedge HE-2 (induced by source paper "discovering high utility episodes in sequences"):
  with members:
    - "proum projection based utility mining on sequence data"
    - "fast utility mining on complex sequences"
    - "a survey of utility oriented pattern mining"
    - "mining closed episodes with simultaneous events"

Connected through hyperedge HE-3 (induced by source paper "a survey of parallel sequential pattern mining"):
  with members:
    - "a survey of utility oriented pattern mining"
    - "desq frequent sequence mining with subsequence constraints"
    - "accelerating dna sequence analysis using intel xeon phi"
    - "the long and the short of it summarising event sequences with serial episodes"

Connected through hyperedge HE-4 (induced by source paper "correlated utility based pattern mining"):
  with members:
    - "huopm high utility occupancy pattern mining"
    - "a survey of parallel sequential pattern mining"
    - "beyond frequency utility mining with varied item specific minimum utility"
    - "a survey of utility oriented pattern mining"

Connected through hyperedge HE-5 (induced by source paper "fast utility mining on complex sequences"):
  with members:
    - "proum projection based utility mining on sequence data"
    - "a survey of parallel sequential pattern mining"
    - "a survey of utility oriented pattern mining"

Connected through hyperedge HE-6 (induced by source paper "proum projection based utility mining on sequence data"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "a survey of utility oriented pattern mining"

Connected through hyperedge HE-7 (induced by source paper "utility mining across multi sequences with individualized thresholds"):
  with members:
    - "huopm high utility occupancy pattern mining"
    - "a survey of parallel sequential pattern mining"

Connected through hyperedge HE-8 (induced by source paper "utility driven mining of trend information for intelligent system"):
  with members:
    - "huopm high utility occupancy pattern mining"

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)