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 "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.

Includes members:
  Member "analysis of stopping active learning based on stabilizing predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "finding eyewitness tweets during crises":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the radical mind identifying signals to detect extremist content on twitter")
  Member "beating the news with embers forecasting civil unrest using open source indicators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "titan a spatiotemporal feature learning framework for traffic incident duration prediction")
  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-4 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  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-5 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")

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.CL(Computation and Language)