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 "anomaly detection and redundancy elimination of big sensor data in internet of things"):
  Abstract: In the era of big data and Internet of things, massive sensor data are gathered with Internet of things. Quantity of data captured by sensor networks are considered to contain highly useful and valuable information. However, for a variety of reasons, received sensor data often appear abnormal. Therefore, effective anomaly detection methods are required to guarantee the quality of data collected by those sensor nodes. Since sensor data are usually correlated in time and space, not all the gathered data are valuable for further data processing and analysis. Preprocessing is necessary for eliminating the redundancy in gathered massive sensor data. In this paper, the proposed work defines a sensor data preprocessing framework. It is mainly composed of two parts, i.e., sensor data anomaly detection and sensor data redundancy elimination. In the first part, methods based on principal statistic analysis and Bayesian network is proposed for sensor data anomaly detection. Then, approaches based on static Bayesian network (SBN) and dynamic Bayesian networks (DBNs) are proposed for sensor data redundancy elimination. Static sensor data redundancy detection algorithm (SSDRDA) for eliminating redundant data in static datasets and real-time sensor data redundancy detection algorithm (RSDRDA) for eliminating redundant sensor data in real-time are proposed. The efficiency and effectiveness of the proposed methods are validated using real-world gathered sensor datasets.

Includes members:
  Member "supervised anomaly detection in uncertain pseudoperiodic data streams":
    also connected through other hyperedges:
      (none)
  Member "characterization of dynamic bayesian network":
    also connected through other hyperedges:
      (none)

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.DC(Distributed, Parallel, and Cluster Computing)