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: "active learning from weak and strong labelers"
  Abstract: An active learner is given a hypothesis class, a large set of unlabeled examples and the ability to interactively query labels to an oracle of a subset of these examples; the goal of the learner is to learn a hypothesis in the class that fits the data well by making as few label queries as possible. #R##N#This work addresses active learning with labels obtained from strong and weak labelers, where in addition to the standard active learning setting, we have an extra weak labeler which may occasionally provide incorrect labels. An example is learning to classify medical images where either expensive labels may be obtained from a physician (oracle or strong labeler), or cheaper but occasionally incorrect labels may be obtained from a medical resident (weak labeler). Our goal is to learn a classifier with low error on data labeled by the oracle, while using the weak labeler to reduce the number of label queries made to this labeler. We provide an active learning algorithm for this setting, establish its statistical consistency, and analyze its label complexity to characterize when it can provide label savings over using the strong labeler alone.

Connected through hyperedge HE-1 (induced by source paper "a graph based approach for active learning in regression"):
  with members:
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "warm starting contextual bandits robustly combining supervised and bandit feedback"):
  with members:
    - "active learning with logged data"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "corralling a band of bandit algorithms"
    - "bandit structured prediction for learning from partial feedback in statistical machine translation"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "normalized online learning"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "bayesian active learning with abstention feedbacks"):
  with members:
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "adaptive maximization of pointwise submodular functions with budget constraint"
    - "efficiency of active learning for the allocation of workers on crowdsourced classification tasks"
    - "robustness of bayesian pool based active learning against prior misspecification"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "noisy bayesian active learning"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "discovering general purpose active learning strategies"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "active one shot learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning to reason end to end module networks for visual question answering"
    - "learning algorithms for active learning"
    - "a meta learning approach to one step active learning"
    - "embodied question answering"

Connected through hyperedge HE-5 (induced by source paper "efficient pac learning from the crowd"):
  with members:
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"

Connected through hyperedge HE-6 (induced by source paper "on line active reward learning for policy optimisation in spoken dialogue systems"):
  with members:
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a neural conversational model"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "active learning from imperfect labelers"):
  with members:
    - "search improves label for active learning"
    - "beyond disagreement based agnostic active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "robust interactive learning"
    - "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)