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: "a new understanding of prediction markets via no regret learning"
  Abstract: We explore the striking mathematical connections that exist between market scoring rules, cost function based prediction markets, and no-regret learning. We show that any cost function based prediction market can be interpreted as an algorithm for the commonly studied problem of learning from expert advice by equating trades made in the market with losses observed by the learning algorithm. If the loss of the market organizer is bounded, this bound can be used to derive an O(sqrt(T)) regret bound for the corresponding learning algorithm. We then show that the class of markets with convex cost functions exactly corresponds to the class of Follow the Regularized Leader learning algorithms, with the choice of a cost function in the market corresponding to the choice of a regularizer in the learning problem. Finally, we show an equivalence between market scoring rules and prediction markets with convex cost functions. This implies that market scoring rules can also be interpreted naturally as Follow the Regularized Leader algorithms, and may be of independent interest. These connections provide new insight into how it is that commonly studied markets, such as the Logarithmic Market Scoring Rule, can aggregate opinions into accurate estimates of the likelihood of future events.

Connected through hyperedge HE-1 (induced by source paper "a decomposition of forecast error in prediction markets"):
  with members:
    - "information aggregation in exponential family markets"
    - "a utility framework for bounded loss market makers"

Connected through hyperedge HE-2 (induced by source paper "crowdsourced outcome determination in prediction markets"):
  with members:
    - "informed truthfulness in multi task peer prediction"
    - "augur a decentralized open source platform for prediction markets"
    - "a utility framework for bounded loss market makers"

Connected through hyperedge HE-3 (induced by source paper "budget constraints in prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "learning performance of prediction markets with kelly bettors"
    - "an optimization based framework for automated market making"

Connected through hyperedge HE-4 (induced by source paper "artificial prediction markets for online prediction of continuous variables a preliminary report"):
  with members:
    - "isoelastic agents and wealth updates in machine learning markets"
    - "machine learning markets"

Connected through hyperedge HE-5 (induced by source paper "privacy and truthful equilibrium selection for aggregative games"):
  with members:
    - "private matchings and allocations"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "privately solving linear programs"
    - "on the complexity of nash equilibria in anonymous games"
    - "privacy preserving public information for sequential games"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "privacy and mechanism design"

Connected through hyperedge HE-6 (induced by source paper "multi period trading prediction markets with connections to machine learning"):
  with members:
    - "toward a market model for bayesian inference"
    - "a utility framework for bounded loss market makers"
    - "isoelastic agents and wealth updates in machine learning markets"
    - "machine learning markets"

Connected through hyperedge HE-7 (induced by source paper "cortical prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "machine learning markets"
    - "a collaborative mechanism for crowdsourcing prediction problems"

Connected through hyperedge HE-8 (induced by source paper "information aggregation in exponential family markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "machine learning markets"

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.AI(Artificial Intelligence)