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 parallel sequential pattern mining"
  Abstract: With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

Connected through hyperedge HE-1 (induced by source paper "probabilistic modeling for novelty detection with applications to fraud identification"):
  with members:
    - "a comparative evaluation of novelty detection algorithms for discrete sequences"
    - "visual interpretability for deep learning a survey"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "neural autoregressive distribution estimation"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "density estimation using real nvp"
    - "variational auto encoded deep gaussian processes"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "a comparative evaluation of novelty detection algorithms for discrete sequences"):
  with members:
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "visual interpretability for deep learning a survey"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "a critical review of recurrent neural networks for sequence learning"
    - "a meta analysis of the anomaly detection problem"
    - "effective approaches to attention based neural machine translation"
    - "visualizing and understanding recurrent networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "generating realistic sequences of customer level transactions for retail datasets"):
  with members:
    - "ecommercegan a generative adversarial network for e commerce"
    - "improved training of wasserstein gans"
    - "customer lifetime value prediction using embeddings"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "correlated utility based pattern mining"):
  with members:
    - "huopm high utility occupancy 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-5 (induced by source paper "fast utility mining on complex sequences"):
  with members:
    - "proum projection based utility mining on sequence data"
    - "a survey of utility oriented 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 utility oriented pattern mining"
    - "privacy preserving utility mining a survey"

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"
    - "privacy preserving utility mining a survey"

Connected through hyperedge HE-8 (induced by source paper "a survey of utility oriented pattern mining"):
  with members:
    - "huopm high utility occupancy pattern mining"
    - "proum projection based utility mining on sequence data"
    - "fast utility mining on complex sequences"
    - "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)