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: "statistical decision making for optimal budget allocation in crowd labeling"
  Abstract: In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and workers have different reliability, it is desirable to have an optimal policy to allocate the budget among all instance-worker pairs such that the overall labeling accuracy is maximized. We consider categorical labeling tasks and formulate the budget allocation problem as a Bayesian Markov decision process (MDP), which simultaneously conducts learning and decision making. Using the dynamic programming (DP) recurrence, one can obtain the optimal allocation policy. However, DP quickly becomes computationally intractable when the size of the problem increases. To solve this challenge, we propose a computationally efficient approximate policy, called optimistic knowledge gradient policy. Our MDP is a quite general framework, which applies to both pull crowdsourcing marketplaces with homogeneous workers and push marketplaces with heterogeneous workers. It can also incorporate the contextual information of instances when they are available. The experiments on both simulated and real data show that the proposed policy achieves a higher labeling accuracy than other existing policies at the same budget level.

Connected through hyperedge HE-1 (induced by source paper "estimating error and bias in offline evaluation results"):
  with members:
    - "recommender systems notation proposed common notation for teaching and research"
    - "the lkpy package for recommender systems experiments next generation tools and lessons learned from the lenskit project"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "thresholding bandit problem with both duels and pulls"):
  with members:
    - "active classification with comparison queries"
    - "thresholding bandits with augmented ucb"
    - "adaptive multiple arm identification"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "buoca budget optimized crowd worker allocation"):
  with members:
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "machine truth serum"):
  with members:
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-5 (induced by source paper "thresholding bandit with optimal aggregate regret"):
  with members:
    - "towards instance optimal bounds for best arm identification"

Connected through hyperedge HE-6 (induced by source paper "inference aided reinforcement learning for incentive mechanism design in crowdsourcing"):
  with members:
    - "argus smartphone enabled human cooperation via multi agent reinforcement learning for disaster situational awareness"
    - "deep reinforcement learning with double q learning"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-7 (induced by source paper "how many workers to ask adaptive exploration for collecting high quality labels"):
  with members:
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)