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: "adaptive crowdsourcing algorithms for the bandit survey problem"
  Abstract: Very recently crowdsourcing has become the de facto platform for distributing and collecting human computation for a wide range of tasks and applications such as information retrieval, natural language processing and machine learning. Current crowdsourcing platforms have some limitations in the area of quality control. Most of the effort to ensure good quality has to be done by the experimenter who has to manage the number of workers needed to reach good results. #R##N#We propose a simple model for adaptive quality control in crowdsourced multiple-choice tasks which we call the \emph{bandit survey problem}. This model is related to, but technically different from the well-known multi-armed bandit problem. We present several algorithms for this problem, and support them with analysis and simulations. Our approach is based in our experience conducting relevance evaluation for a large commercial search engine.

Connected through hyperedge HE-1 (induced by source paper "reply supply efficient crowdsourcing when workers do more than answer questions"):
  with members:
    - "quizz targeted crowdsourcing with a billion potential users"
    - "crowdsourcing predictors of behavioral outcomes"
    - "distributed representations of words and phrases and their compositionality"
    - "time critical social mobilization"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-2 (induced by source paper "a robust ucb scheme for active learning in regression from strategic crowds"):
  with members:
    - "adaptive monte carlo via bandit allocation"
    - "active learning with expert advice"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "active learning with distributional estimates"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "kgeval estimating accuracy of automatically constructed knowledge graphs"):
  with members:
    - "top k querying of unknown values under order constraints"
    - "building hierarchies of concepts via crowdsourcing"
    - "probabilistic similarity logic"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing with unsure option"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "an incentive compatible multi armed bandit crowdsourcing mechanism with quality assurance"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"

Connected through hyperedge HE-5 (induced by source paper "fundamental limits of budget fidelity trade off in label crowdsourcing"):
  with members:
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-6 (induced by source paper "bayesian incentive compatible bandit exploration"):
  with members:
    - "fairness in learning classic and contextual bandits"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "economic recommendation systems"
    - "online learning with feedback graphs beyond bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"
    - "from bandits to experts a tale of domination and independence"

Connected through hyperedge HE-7 (induced by source paper "an optimal bidimensional multi armed bandit auction for multi unit procurement"):
  with members:
    - "bandits with concave rewards and convex knapsacks"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "multi parameter mechanisms with implicit payment computation"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "truthful mechanisms with implicit payment computation"

Connected through hyperedge HE-8 (induced by source paper "regularized minimax conditional entropy for crowdsourcing"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "tensor decompositions for learning latent variable models"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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)