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: "impact of batch size on stopping active learning for text classification"
  Abstract: When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considerations, the use of larger batch sizes is necessary. While past work has shown that larger batch sizes decrease learning efficiency from a learning curve perspective, it remains an open question how batch size impacts methods for stopping active learning. We find that large batch sizes degrade the performance of a leading stopping method over and above the degradation that results from reduced learning efficiency. We analyze this degradation and find that it can be mitigated by changing the window size parameter of how many past iterations of learning are taken into account when making the stopping decision. We find that when using larger batch sizes, stopping methods are more effective when smaller window sizes are used.

Connected through hyperedge HE-1 (induced by source paper "explainable active learning xal an empirical study of how local explanations impact annotator experience"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "stopping active learning based on predicted change of f measure for text classification"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "manipulating and measuring model interpretability"
    - "a unified approach to interpreting model predictions"
    - "machine teaching a new paradigm for building machine learning systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning"):
  with members:
    - "stopping active learning based on predicted change of f measure for text classification"
    - "the use of unlabeled data versus labeled data for stopping active learning for text classification"
    - "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"
    - "filtering tweets for social unrest"
    - "analysis of stopping active learning based on stabilizing predictions"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

Connected through hyperedge HE-3 (induced by source paper "stopping active learning based on predicted change of f measure for text classification"):
  with members:
    - "the use of unlabeled data versus labeled data for stopping active learning for text classification"
    - "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"
    - "learning active learning from data"
    - "filtering tweets for social unrest"
    - "analysis of stopping active learning based on stabilizing predictions"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

Connected through hyperedge HE-4 (induced by source paper "the use of unlabeled data versus labeled data for stopping active learning for text classification"):
  with members:
    - "stopping active learning based on predicted change of f measure for text classification"
    - "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"
    - "filtering tweets for social unrest"
    - "analysis of stopping active learning based on stabilizing predictions"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

Connected through hyperedge HE-5 (induced by source paper "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"):
  with members:
    - "filtering tweets for social unrest"
    - "analysis of stopping active learning based on stabilizing predictions"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

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.LG(Machine Learning)