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: "on the relationship between data efficiency and error for uncertainty sampling"
  Abstract: While active learning offers potential cost savings, the actual data efficiency---the reduction in amount of labeled data needed to obtain the same error rate---observed in practice is mixed. This paper poses a basic question: when is active learning actually helpful? We provide an answer for logistic regression with the popular active learning algorithm, uncertainty sampling. Empirically, on 21 datasets from OpenML, we find a strong inverse correlation between data efficiency and the error rate of the final classifier. Theoretically, we show that for a variant of uncertainty sampling, the asymptotic data efficiency is within a constant factor of the inverse error rate of the limiting classifier.

Connected through hyperedge HE-1 (induced by source paper "adaptive region based active learning"):
  with members:
    - "efficient active learning of sparse halfspaces"
    - "diameter based active learning"
    - "efficient learning of linear separators under bounded noise"
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of 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 "selection via proxy efficient data selection for deep learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "weight agnostic neural networks"
    - "how transferable are the datasets collected by active learners"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "active learning for interactive neural machine translation of data streams"
    - "an empirical study of example forgetting during deep neural network learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-3 (induced by source paper "understanding goal oriented active learning via influence functions"):
  with members:
    - "on the accuracy of influence functions for measuring group effects"
    - "practical obstacles to deploying active learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convergence rates of active learning for maximum likelihood estimation"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-4 (induced by source paper "active learning for network intrusion detection"):
  with members:
    - "deep learning for unsupervised insider threat detection in structured cybersecurity data streams"
    - "recurrent neural network language models for open vocabulary event level cyber anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "on weighted uncertainty sampling in active learning"):
  with members:
    - "bounds on the entropy of a function of a random variable and their applications"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"

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)