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: "beyond disagreement based agnostic active learning"
  Abstract: We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. #R##N#In this paper, we provide such an algorithm. Our solution is based on two novel contributions -- a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and a novel confidence-rated predictor.

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"
    - "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 "bias aware heapified policy for active learning"):
  with members:
    - "dataset distillation"
    - "active adversarial domain adaptation"
    - "adversarial active learning for deep networks a margin based approach"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "leveraging motion priors in videos for improving human segmentation"
    - "discriminative active learning"
    - "learning how to active learn a deep reinforcement learning approach"

Connected through hyperedge HE-3 (induced by source paper "exploring connections between active learning and model extraction"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "practical black box attacks against machine learning"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "efficient active learning of sparse halfspaces"):
  with members:
    - "efficient learning of linear separators under bounded noise"
    - "learning with a drifting target concept"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"
    - "active and passive learning of linear separators under log concave distributions"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-6 (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"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-7 (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"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-8 (induced by source paper "when are epsilon nets small"):
  with members:
    - "epsilon approximations and epsilon nets"
    - "the optimal sample complexity of pac learning"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"

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)