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 "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"):
  Abstract: This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications. Algorithms based on closestto- hyperplane selection and query-by-committee selection are combined with methods for addressing imbalance such as positive amplification based on prevalence statistics from initial random samples. Three algorithms (ClosestPA, QBagPA, and QBoostPA) are presented and carefully evaluated on datasets for text classification and relation extraction. The ClosestPA algorithm is shown to consistently outperform the other two in a variety of ways and insights are provided as to why this is the case.

Includes members:
  Member "impact of batch size on stopping active learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainable active learning xal an empirical study of how local explanations impact annotator experience")
  Member "filtering tweets for social unrest":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "analysis of stopping active learning based on stabilizing predictions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")

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.LG(Machine Learning)