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: "mining all non derivable frequent itemsets"
  Abstract: Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, instead of mining all frequent itemsets. The main goal of this paper is to identify redundancies in the set of all frequent itemsets and to exploit these redundancies in order to reduce the result of a mining operation. We present deduction rules to derive tight bounds on the support of candidate itemsets. We show how the deduction rules allow for constructing a minimal representation for all frequent itemsets. We also present connections between our proposal and recent proposals for concise representations and we give the results of experiments on real-life datasets that show the effectiveness of the deduction rules. In fact, the experiments even show that in many cases, first mining the concise representation, and then creating the frequent itemsets from this representation outperforms existing frequent set mining algorithms.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "discovering compressing serial episodes from event sequences"):
  with members:
    - "the long and the short of it summarising event sequences with serial episodes"

Connected through hyperedge HE-3 (induced by source paper "on when and how to use sat to mine frequent itemsets"):
  with members:
    - "discovering knowledge using a constraint based language"
    - "algorithms for weighted boolean optimization"

Connected through hyperedge HE-4 (induced by source paper "finding good itemsets by packing data"):
  with members:
    - "maximum entropy based significance of itemsets"

Connected through hyperedge HE-5 (induced by source paper "maximum entropy based significance of itemsets"):
  with members:
    - "safe projections of binary data sets"
    - "computational complexity of queries based on itemsets"

Connected through hyperedge HE-6 (induced by source paper "relational association rules getting warmer"):
  with members:
    - "efficient and extensible algorithms for multi query optimization"

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)