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: "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
  Abstract: Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing imbalance is to base cost models during AL on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the labeled training data, which is the leading method used during PL.

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"
    - "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 "corpus wide argument mining a working solution"):
  with members:
    - "fast end to end wikification"
    - "are you convinced choosing the more convincing evidence with a siamese network"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural end to end learning for computational argumentation mining"
    - "parsing argumentation structures in persuasive essays"
    - "an empirical evaluation of various deep learning architectures for bi sequence classification tasks"
    - "active learning with statistical models"

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"
    - "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 "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"
    - "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"
    - "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-6 (induced by source paper "filtering tweets for social unrest"):
  with members:
    - "analysis of stopping active learning based on stabilizing predictions"
    - "finding eyewitness tweets during crises"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

Connected through hyperedge HE-7 (induced by source paper "analysis of stopping active learning based on stabilizing predictions"):
  with members:
    - "bucking the trend large scale cost focused active learning for statistical machine translation"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "committee based sample selection for probabilistic classifiers"
    - "active learning with statistical models"

Connected through hyperedge HE-8 (induced by source paper "bucking the trend large scale cost focused active learning for statistical machine translation"):
  with members:
    - "using mechanical turk to build machine translation evaluation sets"
    - "an approach to reducing annotation costs for bionlp"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "rule writing or annotation cost efficient resource usage for base noun phrase chunking"

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)