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: "near optimal active learning of halfspaces via query synthesis in the noisy setting"
  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). 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, 3) appropriately aggregates the results and outputs a linear classifier, and 4) provides a theoretical guarantee for all possible schemes of aggregation. The proposed method is proved resilient to noise. We show that the DC framework avoids the curse of dimensionality: its computational complexity scales linearly with the dimension. Moreover, we show that the query complexity of DC is near optimal (within a constant factor of the optimum algorithm). To further support our theoretical analysis, we compare the performance of DC with the existing work. 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 learning for binary classification with abstention"):
  with members:
    - "bandits and experts in metric spaces"
    - "x armed bandits"

Connected through hyperedge HE-2 (induced by source paper "exploring connections between active learning and model extraction"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "active decision boundary annotation with deep generative models"):
  with members:
    - "generative adversarial active learning"
    - "structured receptive fields in cnns"
    - "dimension coupling optimal active learning of halfspaces via query synthesis"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "revisiting perceptron efficient and label optimal learning of halfspaces"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "efficient and parsimonious agnostic active learning"
    - "complexity theoretic limitations on learning halfspaces"
    - "active and passive learning of linear separators under log concave distributions"
    - "active learning using smooth relative regret approximations with applications"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-5 (induced by source paper "active expansion sampling for learning feasible domains in an unbounded input space"):
  with members:
    - "hierarchical subquery evaluation for active learning on a graph"
    - "exponentiated gradient exploration for active learning"
    - "committee based sample selection for probabilistic classifiers"

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)