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: "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
  Abstract: A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.

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"
    - "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "overcoming practical issues of deep active learning and its applications on named entity recognition"):
  with members:
    - "practical obstacles to deploying active learning"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning algorithms for active learning"
    - "deep learning scaling is predictable empirically"
    - "distributed representations of words and phrases and their compositionality"

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"
    - "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"

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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "ed2 two stage active learning for error detection technical report"):
  with members:
    - "boostclean automated error detection and repair for machine learning"
    - "holoclean holistic data repairs with probabilistic inference"
    - "xgboost a scalable tree boosting system"
    - "truth finding on the deep web is the problem solved"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "limitations of assessing active learning performance at runtime"):
  with members:
    - "estimating accuracy from unlabeled data a probabilistic logic approach"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"

Connected through hyperedge HE-7 (induced by source paper "support vector machine active learning algorithms with query by committee versus closest to hyperplane selection"):
  with members:
    - "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"

Connected through hyperedge HE-8 (induced by source paper "impact of batch size on stopping active learning for text classification"):
  with members:
    - "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"

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)