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 "stopping active learning based on predicted change of f measure for text classification"):
  Abstract: During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted Change of F Measure will be introduced that attempts to provide the users an estimate of how much performance of the model is changing at each iteration. This stopping method can be applied with any base learner. This method is useful for reducing the data annotation bottleneck encountered when building text classification systems.

Includes members:
  Member "the use of unlabeled data versus labeled data for stopping active learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection":
    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 "impact of batch size on stopping active learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainable active learning xal an empirical study of how local explanations impact annotator experience")
  Member "learning active learning from data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action for better prediction")
  Member "filtering tweets for social unrest":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-7 (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)