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: "efficient active learning of halfspaces an aggressive approach"
  Abstract: We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.

Connected through hyperedge HE-1 (induced by source paper "flattening a hierarchical clustering through active learning"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "diameter based active learning"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "clustering with same cluster queries"
    - "local algorithms for interactive clustering"
    - "agnostic active learning without constraints"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-2 (induced by source paper "exploring representativeness and informativeness for active learning"):
  with members:
    - "multi view intact space learning"
    - "hierarchical subquery evaluation for active learning on a graph"

Connected through hyperedge HE-3 (induced by source paper "a structured perspective of volumes on active learning"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "exploring representativeness and informativeness for active learning"
    - "diameter based active learning"
    - "learning algorithms for active learning"
    - "active discriminative text representation learning"

Connected through hyperedge HE-4 (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"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"

Connected through hyperedge HE-5 (induced by source paper "submodular learning and covering with response dependent costs"):
  with members:
    - "trading off worst and expected cost in decision tree problems and a value dependent model"
    - "auditing active learning with outcome dependent query costs"
    - "greedy feature selection for subspace clustering"
    - "near optimal bayesian active learning with noisy observations"
    - "interactive submodular set cover"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "active nearest neighbor learning in metric spaces"):
  with members:
    - "exact lower bounds for the agnostic probably approximately correct pac machine learning model"
    - "maximum margin multiclass nearest neighbors"
    - "minimax analysis of active learning"
    - "active learning on trees and graphs"
    - "adaptive metric dimensionality reduction"

Connected through hyperedge HE-7 (induced by source paper "from cutting planes algorithms to compression schemes and active learning"):
  with members:
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-8 (induced by source paper "beating the minimax rate of active learning with prior knowledge"):
  with members:
    - "the power of localization for efficiently learning linear separators with noise"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

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)