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: "dimension coupling optimal active learning of halfspaces via query synthesis"
  Abstract: In this paper, we consider the problem of actively learning a linear classifier through query synthesis where the learner can construct artificial queries in order to estimate the true decision boundaries. This problem has recently gained a lot of interest in automated science and adversarial reverse engineering for which only heuristic algorithms are known. In such applications, queries can be constructed de novo to elicit information (e.g., automated science) or to evade detection with minimal cost (e.g., adversarial reverse engineering). #R##N#We develop a general framework, called dimension coupling (DC), that 1) reduces a d-dimensional learning problem to d-1 low-dimensional sub-problems, 2) solves each sub-problem efficiently, and 3) appropriately aggregates the results and outputs a linear classifier. We consider the three most common scenarios in the literature: idealized noise-free, independent noise realizations, and agnostic settings. We show that the DC framework avoids the curse of dimensionality: its computational complexity in all three cases scales linearly with the dimension. Moreover, in the noiseless and noisy cases, we show that the query complexity of DC is near optimal (within a constant factor of the optimum algorithm). We also develop an agnostic variant of DC for which we provide strong theoretical guarantees. To further support our theoretical analysis, we compare the performance of DC with the existing work in all three settings. We observe that DC consistently outperforms the prior arts in terms of query complexity while often running orders of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "active classification with comparison queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "clustering with same cluster queries"
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-2 (induced by source paper "active decision boundary annotation with deep generative models"):
  with members:
    - "generative adversarial active learning"
    - "structured receptive fields in cnns"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "ambient sound provides supervision for visual learning"
    - "adversarial feature learning"
    - "spot on action localization from pointly supervised proposals"
    - "context encoders feature learning by inpainting"
    - "generative visual manipulation on the natural image manifold"

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.AI(Artificial Intelligence)