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: "minimax analysis of active learning"
  Abstract: This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models. The results reveal a number of surprising facts. In particular, under the noise model of Tsybakov (2004), the minimax label complexity of active learning with a VC class is always asymptotically smaller than that of passive learning, and is typically significantly smaller than the best previously-published upper bounds in the active learning literature. In high-noise regimes, it turns out that all active learning problems of a given VC dimension have roughly the same minimax label complexity, which contrasts with well-known results for bounded noise. In low-noise regimes, we find that the label complexity is well-characterized by a simple combinatorial complexity measure we call the star number. Interestingly, we find that almost all of the complexity measures previously explored in the active learning literature have worst-case values exactly equal to the star number. We also propose new active learning strategies that nearly achieve these minimax label complexities.

Connected through hyperedge HE-1 (induced by source paper "adaptive region based active learning"):
  with members:
    - "efficient active learning of sparse halfspaces"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "diameter based active learning"
    - "efficient learning of linear separators under bounded noise"
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-2 (induced by source paper "noise tolerant reliable active classification with comparison queries"):
  with members:
    - "the power of comparisons for actively learning linear classifiers"
    - "sorting by swaps with noisy comparisons"
    - "generalized comparison trees for point location problems"
    - "optimal sorting with persistent comparison errors"
    - "active classification with comparison queries"
    - "efficient learning of linear separators under bounded noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (induced by source paper "k nn active learning under local smoothness assumption"):
  with members:
    - "five proofs of chernoff s bound with applications"

Connected through hyperedge HE-4 (induced by source paper "fast rate pac bayes generalization bounds via shifted rademacher processes"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "exploring generalization in deep learning"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "a strongly quasiconvex pac bayesian bound"

Connected through hyperedge HE-5 (induced by source paper "minimax optimal online stochastic learning for sequences of convex functions under sub gradient observation failures"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization with biased noisy gradient oracles"
    - "scale free online learning"
    - "threshold effects in parameter estimation from compressed data"
    - "online learning with feedback graphs beyond bandits"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "online and batch learning algorithms for data with missing features"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "disagreement based active learning in online settings"):
  with members:
    - "efficient active learning of sparse halfspaces"
    - "efficient learning of linear separators under bounded noise"
    - "the power of localization for efficiently learning linear separators with noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "importance weighted active learning"

Connected through hyperedge HE-7 (induced by source paper "active learning with logged data"):
  with members:
    - "effective evaluation using logged bandit feedback from multiple loggers"
    - "recommendations as treatments debiasing learning and evaluation"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "online importance weight aware updates"

Connected through hyperedge HE-8 (induced by source paper "active learning for cost sensitive classification"):
  with members:
    - "practical contextual bandits with regression oracles"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "efficient algorithms for adversarial contextual learning"
    - "efficient and parsimonious agnostic active learning"
    - "learning to search better than your teacher"
    - "going deeper with convolutions"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "beyond disagreement based agnostic 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)