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: "integrity verification for outsourcing uncertain frequent itemset mining"
  Abstract: In recent years, due to the wide applications of uncertain data (e.g., noisy data), uncertain frequent itemsets (UFI) mining over uncertain databases has attracted much attention, which differs from the corresponding deterministic problem from the generalized definition and resolutions. As the most costly task in association rule mining process, it has been shown that outsourcing this task to a service provider (e.g.,the third cloud party) brings several benefits to the data owner such as cost relief and a less commitment to storage and computational resources. However, the correctness integrity of mining results can be corrupted if the service provider is with random fault or not honest (e.g., lazy, malicious, etc). Therefore, in this paper, we focus on the integrity and verification issue in UFI mining problem during outsourcing process, i.e., how the data owner verifies the mining results. Specifically, we explore and extend the existing work on deterministic FI outsourcing verification to uncertain scenario. For this purpose, We extend the existing outsourcing FI mining work to uncertain area w.r.t. the two popular UFI definition criteria and the approximate UFI mining methods. Specifically, We construct and improve the basic/enhanced verification scheme with such different UFI definition respectively. After that, we further discuss the scenario of existing approximation UFP mining, where we can see that our technique can provide good probabilistic guarantees about the correctness of the verification. Finally, we present the comparisons and analysis on the schemes proposed in this paper.

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)