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 and parsimonious agnostic active learning"
  Abstract: We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previous approaches satisfying 1 and 2. To do this we create an algorithm based on a newly defined optimization problem and analyze it. We also conduct the first experimental analysis of all efficient agnostic active learning algorithms, evaluating their strengths and weaknesses in different settings.

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"
    - "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 "deep active learning with a neural architecture search"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "cost effective active learning for deep image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (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"
    - "beyond disagreement based agnostic active learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "minimax analysis of active learning"
    - "online importance weight aware updates"

Connected through hyperedge HE-4 (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"
    - "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"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-5 (induced by source paper "revisiting perceptron efficient and label optimal learning of halfspaces"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "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-6 (induced by source paper "deep active learning over the long tail"):
  with members:
    - "deep bayesian active learning with image data"
    - "coresets and sketches"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "search improves label for active learning"):
  with members:
    - "beyond disagreement based agnostic active learning"
    - "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)