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: "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.

Connected through hyperedge HE-1 (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"
    - "impact of batch size on stopping active learning for text classification"
    - "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-2 (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"
    - "impact of batch size on stopping active learning for text classification"
    - "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-3 (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"
    - "impact of batch size on stopping active learning for text classification"
    - "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-4 (induced by source paper "impact of batch size on stopping active learning for text classification"):
  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)