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: "representing aggregate belief through the competitive equilibrium of a securities market"
  Abstract: We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is best the general consensus is that there is no consensus. We develop a market-based approach to this problem, where agents bet on uncertain events by buying or selling securities contingent on their outcomes. Each agent acts in the market so as to maximize expected utility at given securities prices, limited in its activity only by its own risk aversion. The equilibrium prices of goods in this market represent aggregate beliefs. For agents with constant risk aversion, we demonstrate that the aggregate probability exhibits several desirable properties, and is related to independently motivated techniques. We argue that the market-based approach provides a plausible mechanism for belief aggregation in multiagent systems, as it directly addresses self-motivated agent incentives for participation and for truthfulness, and can provide a decision-theoretic foundation for the "expert weights" often employed in centralized pooling techniques.

Connected through hyperedge HE-1 (induced by source paper "designing informative securities"):
  with members:
    - "compact securities markets for pareto optimal reallocation of risk"
    - "a collaborative mechanism for crowdsourcing prediction problems"
    - "a new understanding of prediction markets via no regret learning"
    - "an optimization based framework for automated market making"
    - "complexity of combinatorial market makers"

Connected through hyperedge HE-2 (induced by source paper "isoelastic agents and wealth updates in machine learning markets"):
  with members:
    - "an empirical comparison of algorithms for aggregating expert predictions"
    - "learning performance of prediction markets with kelly bettors"
    - "machine learning markets"
    - "a collaborative mechanism for crowdsourcing prediction problems"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-3 (induced by source paper "an empirical comparison of algorithms for aggregating expert predictions"):
  with members:
    - "on line prediction with kernels and the complexity approximation principle"
    - "a generative bayesian model for aggregating experts probabilities"

Connected through hyperedge HE-4 (induced by source paper "machine learning markets"):
  with members:
    - "probabilistic latent semantic analysis"
    - "an empirical comparison of algorithms for aggregating expert predictions"
    - "a new understanding of prediction markets via no regret learning"

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)