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: "an empirical comparison of algorithms for aggregating expert predictions"
  Abstract: Predicting the outcomes of future events is a challenging problem for which a variety of solution methods have been explored and attempted. We present an empirical comparison of a variety of online and offline adaptive algorithms for aggregating experts' predictions of the outcomes of five years of US National Football League games (1319 games) using expert probability elicitations obtained from an Internet contest called ProbabilitySports. We find that it is difficult to improve over simple averaging of the predictions in terms of prediction accuracy, but that there is room for improvement in quadratic loss. Somewhat surprisingly, a Bayesian estimation algorithm which estimates the variance of each expert's prediction exhibits the most consistent superior performance over simple averaging among our collection of algorithms.

Connected through hyperedge HE-1 (induced by source paper "team selection for prediction tasks"):
  with members:
    - "eliciting forecasts from self interested experts scoring rules for decision makers"

Connected through hyperedge HE-2 (induced by source paper "isoelastic agents and wealth updates in machine learning markets"):
  with members:
    - "representing aggregate belief through the competitive equilibrium of a securities market"
    - "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 "twitterpaul extracting and aggregating twitter predictions"):
  with members:
    - "why watching movie tweets won t tell the whole story"
    - "i wanted to predict elections with twitter and all i got was this lousy paper a balanced survey on election prediction using twitter data"
    - "a survey of prediction using social media"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "machine learning markets"):
  with members:
    - "probabilistic latent semantic analysis"
    - "representing aggregate belief through the competitive equilibrium of a securities market"
    - "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)