human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "macrobase analytic monitoring for the internet of things"):
  Abstract: An increasing proportion of data today is generated by automated processes, sensors, and systems---collectively, the Internet of Things (IoT). A key challenge in IoT data management and a core aspect of many IoT applications (e.g., industrial diagnostics, predictive maintenance, and equipment auditing) is in identifying and highlighting unusual and surprising data (e.g., poor driving behavior, equipment failures, physical intrusion). We call this task, which is often statistical in nature and time-sensitive, analytic monitoring. To facilitate rapid development and scalable deployment of analytic monitoring queries, we have developed MacroBase, a data analytics engine that performs analytic monitoring of IoT data streams. MacroBase implements a customizable pipeline of outlier detection, summarization, and ranking operators. For efficient and accurate execution, MacroBase implements several cross-layer optimizations across robust estimation, pattern mining, and sketching procedures, allowing order-of-magnitude speedups. As a result, MacroBase can analyze up to 1M events per second on a single core. MacroBase has already delivered meaningful analytic monitoring results in production at a medium-scale IoT startup.

Includes members:
  Member "incremental knowledge base construction using deepdive":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")
  Member "towards a unified architecture for in rdbms analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "identifying the signs of fraudulent accounts using data mining techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "weighted random sampling over data streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication efficient weighted reservoir sampling")

Question: Please tell me which class the source paper 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)