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: "price prediction in a trading agent competition"
  Abstract: The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.

Connected through hyperedge HE-1 (induced by source paper "optimal strategies for simultaneous vickrey auctions with perfect substitutes"):
  with members:
    - "bidding under uncertainty theory and experiments"
    - "decision theoretic bidding based on learned density models in simultaneous interacting auctions"

Connected through hyperedge HE-2 (induced by source paper "self confirming price prediction strategies for simultaneous one shot auctions"):
  with members:
    - "bayesian games and the smoothness framework"
    - "the structure of signals causal interdependence models for games of incomplete information"
    - "self confirming price prediction for bidding in simultaneous ascending auctions"
    - "bidding under uncertainty theory and experiments"
    - "simultaneous auctions are almost efficient"
    - "roxybot 06 stochastic prediction and optimization in tac travel"
    - "decision theoretic bidding based on learned density models in simultaneous interacting auctions"

Connected through hyperedge HE-3 (induced by source paper "soft gaussian cde regression models and loss functions"):
  with members:
    - "regression conformal prediction with nearest neighbours"
    - "a tutorial on conformal prediction"
    - "types of cost in inductive concept learning"

Connected through hyperedge HE-4 (induced by source paper "bidding under uncertainty theory and experiments"):
  with members:
    - "decision theoretic bidding based on learned density models in simultaneous interacting auctions"

Connected through hyperedge HE-5 (induced by source paper "roxybot 06 stochastic prediction and optimization in tac travel"):
  with members:
    - "bidding under uncertainty theory and experiments"

Connected through hyperedge HE-6 (induced by source paper "decision theoretic bidding based on learned density models in simultaneous interacting auctions"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "attac 2000 an adaptive autonomous bidding agent"
    - "gib imperfect information in a computationally challenging game"

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)