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: "enabling smart data noise filtering in big data classification"
  Abstract: In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem.

Connected through hyperedge HE-1 (induced by source paper "smart data based ensemble for imbalanced big data classification"):
  with members:
    - "scarff a scalable framework for streaming credit card fraud detection with spark"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"
    - "mllib machine learning in apache spark"
    - "what will 5g be"
    - "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "the role of big data analytics in industrial internet of things"):
  with members:
    - "mobile cloud business process management system for the internet of things a survey"
    - "graph of virtual actors gova a big data analytics architecture for iot"
    - "valorising the iot databox creating value for everyone"
    - "ifogsim a toolkit for modeling and simulation of resource management techniques in internet of things edge and fog computing environments"
    - "mllib machine learning in apache spark"
    - "the emerging internet of things marketplace from an industrial perspective a survey"
    - "a survey on internet of things from industrial market perspective"

Connected through hyperedge HE-3 (induced by source paper "dpasf a flink library for streaming data preprocessing"):
  with members:
    - "feature selection a data perspective"

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)