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: "prediction markets mechanism design and cooperative game theory"
  Abstract: Prediction markets are designed to elicit information from multiple agents in order to predict (obtain probabilities for) future events. A good prediction market incentivizes agents to reveal their information truthfully; such incentive compatibility considerations are commonly studied in mechanism design. While this relation between prediction markets and mechanism design is well understood at a high level, the models used in prediction markets tend to be somewhat different from those used in mechanism design. This paper considers a model for prediction markets that fits more straightforwardly into the mechanism design framework. We consider a number of mechanisms within this model, all based on proper scoring rules. We discuss basic properties of these mechanisms, such as incentive compatibility. We also draw connections between some of these mechanisms and cooperative game theory. Finally, we speculate how one might build a practical prediction market based on some of these ideas.

Connected through hyperedge HE-1 (induced by source paper "incentive compatible active learning"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "learning time dependent choice"
    - "low cost learning via active data procurement"
    - "learning economic parameters from revealed preferences"
    - "efficiently learning from revealed preference"

Connected through hyperedge HE-2 (induced by source paper "strategy proof incentives for predictions"):
  with members:
    - "when should an expert make a prediction"

Connected through hyperedge HE-3 (induced by source paper "parametric prediction from parametric agents"):
  with members:
    - "low cost learning via active data procurement"

Connected through hyperedge HE-4 (induced by source paper "double or nothing multiplicative incentive mechanisms for crowdsourcing"):
  with members:
    - "competitive analysis of the top k ranking problem"
    - "reliable crowdsourcing under the generalized dawid skene model"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"

Connected through hyperedge HE-5 (induced by source paper "solving cooperative reliability games"):
  with members:
    - "measuring inconsistency in probabilistic knowledge bases"
    - "the cost of stability in coalitional games"
    - "complexity of determining nonemptiness of the core"

Connected through hyperedge HE-6 (induced by source paper "strategy proof prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "a new understanding of prediction markets via no regret learning"
    - "an optimization based framework for automated market making"

Connected through hyperedge HE-7 (induced by source paper "eliciting forecasts from self interested experts scoring rules for decision makers"):
  with members:
    - "a utility framework for bounded loss market makers"

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)