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 compression technique for analyzing disagreement based active learning"
  Abstract: We introduce a new and improved characterization of the label complexity of disagreement-based active learning, in which the leading quantity is the version space compression set size. This quantity is defined as the size of the smallest subset of the training data that induces the same version space. We show various applications of the new characterization, including a tight analysis of CAL and refined label complexity bounds for linear separators under mixtures of Gaussians and axis-aligned rectangles under product densities. The version space compression set size, as well as the new characterization of the label complexity, can be naturally extended to agnostic learning problems, for which we show new speedup results for two well known active learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep active learning with a neural architecture search"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "cost effective active learning for deep image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "active classification with comparison queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "dimension coupling optimal active learning of halfspaces via query synthesis"
    - "clustering with same cluster queries"
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-3 (induced by source paper "refined error bounds for several learning algorithms"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "active learning using smooth relative regret approximations with applications"

Connected through hyperedge HE-4 (induced by source paper "minimax analysis of active learning"):
  with members:
    - "the power of localization for efficiently learning linear separators with noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "active learning using smooth relative regret approximations with applications"
    - "robust interactive learning"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"

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)