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: "filtering tweets for social unrest"
  Abstract: Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest prediction, we focus on filtering the vast volume of tweets to identify tweets relevant to unrest, which can be provided to downstream users for further analysis. We train a supervised classifier that is able to label Arabic language tweets as relevant to unrest with high reliability. We examine the relationship between training data size and performance and investigate ways to optimize the model building process while minimizing cost. We also explore how confidence thresholds can be set to achieve desired levels of performance.

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"
    - "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"
    - "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"
    - "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"
    - "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"
    - "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 "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"
    - "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 "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"
    - "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.CL(Computation and Language)