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: "a survey of utility oriented pattern mining"
  Abstract: The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

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

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"
    - "privacy preserving utility mining a survey"
    - "mining closed episodes with simultaneous events"

Connected through hyperedge HE-3 (induced by source paper "a survey of parallel sequential pattern mining"):
  with members:
    - "privacy preserving utility mining a survey"
    - "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"
    - "privacy preserving utility mining a survey"

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"
    - "privacy preserving utility mining a survey"

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"
    - "privacy preserving utility mining a survey"

Connected through hyperedge HE-7 (induced by source paper "utility mining across multi dimensional sequences"):
  with members:
    - "huopm high utility occupancy pattern mining"
    - "top k route search through submodularity modeling of recurrent poi features"

Connected through hyperedge HE-8 (induced by source paper "utility driven data analytics on uncertain data"):
  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)