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: "huopm high utility occupancy pattern mining"
  Abstract: Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure to identify high-quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria, such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, that is, they often represent small sets of items in transactions containing many items and, thus, may not be truly representative of these transactions. To extract high-quality patterns in real-life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named high-utility occupancy pattern mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel frequency-utility tree and two compact data structures, called the utility-occupancy list and frequency-utility table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high-quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable, and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.

Connected through hyperedge HE-1 (induced by source paper "correlated utility based pattern mining"):
  with members:
    - "a survey of parallel sequential pattern mining"
    - "beyond frequency utility mining with varied item specific minimum utility"
    - "a survey of utility oriented pattern mining"
    - "privacy preserving utility mining a survey"

Connected through hyperedge HE-2 (induced by source paper "utility mining across multi dimensional sequences"):
  with members:
    - "a survey of utility oriented pattern mining"
    - "top k route search through submodularity modeling of recurrent poi features"

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

Connected through hyperedge HE-4 (induced by source paper "utility driven mining of trend information for intelligent system"):
  with members:
    - "privacy preserving utility mining a survey"

Connected through hyperedge HE-5 (induced by source paper "utility driven data analytics on uncertain data"):
  with members:
    - "a survey of utility oriented pattern mining"

Connected through hyperedge HE-6 (induced by source paper "a survey of utility oriented pattern mining"):
  with members:
    - "proum projection based utility mining on sequence data"
    - "fast utility mining on complex sequences"
    - "a survey of parallel sequential pattern mining"
    - "privacy preserving utility mining a survey"
    - "maximum entropy models and subjective interestingness an application to tiles in binary databases"
    - "mining all non derivable frequent itemsets"

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)