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: "industrial big data analytics challenges methodologies and applications"
  Abstract: While manufacturers have been generating highly #R##N#distributed data from various systems, devices and applications, a #R##N#number of challenges in both data management and data analysis #R##N#require new approaches to support the big data era. These #R##N#challenges for industrial big data analytics is real-time analysis #R##N#and decision-making from massive heterogeneous data sources in #R##N#manufacturing space. This survey presents new concepts, methodologies, #R##N#and applications scenarios of industrial big data analytics, #R##N#which can provide dramatic improvements in velocity and veracity #R##N#problem solving. We focus on five important methodologies of #R##N#industrial big data analytics: 1) Highly distributed industrial data #R##N#ingestion: access and integrate to highly distributed data sources #R##N#from from various systems, devices and applications; 2) Industrial #R##N#big data repository: cope with sampling biases and heterogeneity, #R##N#and store different data formats and structures; 3) Large-scale #R##N#industrial data management: organizes massive heterogeneous #R##N#data and share large-scale data; 4) Industrial data analytics: track #R##N#data provenance, from data generation through data preparation; #R##N#5) Industrial data governance: ensures data trust, integrity and #R##N#security. For each phase, we introduce to current research in #R##N#industries and academia, and discusses challenges and potential #R##N#solutions. We also examine the typical applications of industrial #R##N#big data, including smart factory visibility, machine fleet, energy #R##N#management, proactive maintenance, and just in time supply #R##N#chain. These discussions aim to understand the value of industrial #R##N#big data. Lastly, this survey is concluded with a discussion of open #R##N#problems and future directions.

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)