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: "finish them pricing algorithms for human computation"
  Abstract: Given a batch of human computation tasks, a commonly ignored aspect is how the price (i.e., the reward paid to human workers) of these tasks must be set or varied in order to meet latency or cost constraints. Often, the price is set up-front and not modified, leading to either a much higher monetary cost than needed (if the price is set too high), or to a much larger latency than expected (if the price is set too low). Leveraging a pricing model from prior work, we develop algorithms to optimally set and then vary price over time in order to meet a (a) user-specified deadline while minimizing total monetary cost (b) user-specified monetary budget constraint while minimizing total elapsed time. We leverage techniques from decision theory (specifically, Markov Decision Processes) for both these problems, and demonstrate that our techniques lead to upto 30\% reduction in cost over schemes proposed in prior work. Furthermore, we develop techniques to speed-up the computation, enabling users to leverage the price setting algorithms on-the-fly.

Connected through hyperedge HE-1 (induced by source paper "crowd powered data mining"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "multi label annotation aggregation in crowdsourcing"
    - "clustering via crowdsourcing"
    - "clamshell speeding up crowds for low latency data labeling"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "comprehensive and reliable crowd assessment algorithms"
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-2 (induced by source paper "octopus a framework for cost quality time optimization in crowdsourcing"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "point based pomdp algorithms improved analysis and implementation"
    - "crowdsourcing control moving beyond multiple choice"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "understanding workers developing effective tasks and enhancing marketplace dynamics a study of a large crowdsourcing marketplace"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "cdas a crowdsourcing data analytics system"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-4 (induced by source paper "optimizing open ended crowdsourcing the next frontier in crowdsourced data management"):
  with members:
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "a large annotated corpus for learning natural language inference"
    - "comprehensive and reliable crowd assessment algorithms"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowdsourcing control moving beyond multiple choice"
    - "cdas a crowdsourcing data analytics system"
    - "human assisted graph search it s okay to ask questions"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "frog a fast and reliable crowdsourcing framework technical report"):
  with members:
    - "octopus a framework for cost quality time optimization in crowdsourcing"
    - "clamshell speeding up crowds for low latency data labeling"
    - "crowdsourcing a word emotion association lexicon"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "whom to ask jury selection for decision making tasks on micro blog services"
    - "analytic methods for optimizing realtime crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-6 (induced by source paper "clamshell speeding up crowds for low latency data labeling"):
  with members:
    - "mllib machine learning in apache spark"
    - "crowder crowdsourcing entity resolution"
    - "analytic methods for optimizing realtime crowdsourcing"
    - "human powered sorts and joins"
    - "active learning with statistical models"

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.GT(Computer Science and Game Theory)