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 "the use of unlabeled data versus labeled data for stopping active learning for text classification"):
  Abstract: Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing when to stop active learning are an additional labeled set of data, an unlabeled set of data, and the training data that is labeled during the process of active learning. To date, no one has compared and contrasted the advantages and disadvantages of stopping methods based on these three information sources. We find that stopping methods that use unlabeled data are more effective than methods that use labeled data.

Includes members:
  Member "stopping active learning based on predicted change of f measure 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 "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 "filtering tweets for social unrest":
    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 "analysis of stopping active learning based on stabilizing predictions":
    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 "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-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)