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: "optimal crowdsourcing contests"
  Abstract: We study the design and approximation of optimal crowdsourcing contests. Crowdsourcing contests can be modeled as all-pay auctions because entrants must exert effort up-front to enter. Unlike all-pay auctions where a usual design objective would be to maximize revenue, in crowdsourcing contests, the principal only benefits from the submission with the highest quality. We give a theory for optimal crowdsourcing contests that mirrors the theory of optimal auction design: the optimal crowdsourcing contest is a virtual valuation optimizer (the virtual valuation function depends on the distribution of contestant skills and the number of contestants). We also compare crowdsourcing contests with more conventional means of procurement. In this comparison, crowdsourcing contests are relatively disadvantaged because the effort of losing contestants is wasted. Nonetheless, we show that crowdsourcing contests are 2-approximations to conventional methods for a large family of "regular" distributions, and 4-approximations, otherwise.

Connected through hyperedge HE-1 (induced by source paper "collaborative data acquisition"):
  with members:
    - "selling multiple items via social networks"
    - "multi winner contests for strategic diffusion in social networks"
    - "mechanism design in social networks"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "time critical social mobilization"

Connected through hyperedge HE-2 (induced by source paper "competitive statistical estimation with strategic data sources"):
  with members:
    - "optimal data acquisition for statistical estimation"
    - "statistical estimation with strategic data sources in competitive settings"
    - "informed truthfulness in multi task peer prediction"
    - "promoting truthful behavior in participatory sensing mechanisms"
    - "estimation with strategic sensors"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "optimal mechanisms for selling information"

Connected through hyperedge HE-3 (induced by source paper "optimal mechanisms with budget for user generated contents"):
  with members:
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"
    - "crowdsourcing with endogenous entry"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing data acquisition via social networks"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "time critical social mobilization"

Connected through hyperedge HE-5 (induced by source paper "beyond monetary incentives experiments in paid microtask contests modelled as continuous time markov chains"):
  with members:
    - "analytic methods for optimizing realtime crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "on the posted pricing in crowdsourcing power of bonus"):
  with members:
    - "optimal posted prices for online cloud resource allocation"
    - "bayesian budget feasibility with posted pricing"
    - "approval voting and incentives in crowdsourcing"
    - "incentivizing high quality crowdwork"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "reputation based incentive protocols in crowdsourcing applications"

Connected through hyperedge HE-7 (induced by source paper "certified computation in crowdsourcing"):
  with members:
    - "being robust in high dimensions can be practical"
    - "learning from untrusted data"
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
    - "feature multi selection among subjective features"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-8 (induced by source paper "agent failures in all pay auctions"):
  with members:
    - "solving cooperative reliability games"

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)