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 "early forecasting of text classification accuracy and f measure with active learning"):
  Abstract: When creating text classification systems, one of the major bottlenecks is the annotation of training data. Active learning has been proposed to address this bottleneck using stopping methods to minimize the cost of data annotation. An important capability for improving the utility of stopping methods is to effectively forecast the performance of the text classification models. Forecasting can be done through the use of logarithmic models regressed on some portion of the data as learning is progressing. A critical unexplored question is what portion of the data is needed for accurate forecasting. There is a tension, where it is desirable to use less data so that the forecast can be made earlier, which is more useful, versus it being desirable to use more data, so that the forecast can be more accurate. We find that when using active learning it is even more important to generate forecasts earlier so as to make them more useful and not waste annotation effort. We investigate the difference in forecasting difficulty when using accuracy and F-measure as the text classification system performance metrics and we find that F-measure is more difficult to forecast. We conduct experiments on seven text classification datasets in different semantic domains with different characteristics and with three different base machine learning algorithms. We find that forecasting is easiest for decision tree learning, moderate for Support Vector Machines, and most difficult for neural networks.

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 "the use of unlabeled data versus labeled data for stopping active learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stopping active learning based on predicted change of f measure for text classification")
  Member "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stopping active learning based on predicted change of f measure for text classification")
  Member "impact of batch size on stopping active learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "stopping active learning based on predicted change of f measure for text classification")
  Member "analysis of stopping active learning based on stabilizing predictions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stopping active learning based on predicted change of f measure for text classification")
  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-7 (induced by source paper "corpus wide argument mining a working solution")
  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-8 (induced by source paper "overcoming practical issues of deep active learning and its applications on named entity recognition")

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.IR(Information Retrieval)