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: "online prediction with selfish experts"
  Abstract: We consider the problem of binary prediction with expert advice in settings where experts have agency and seek to maximize their credibility. This paper makes three main contributions. First, it defines a model to reason formally about settings with selfish experts, and demonstrates that "incentive compatible" (IC) algorithms are closely related to the design of proper scoring rules. Designing a good IC algorithm is easy if the designer's loss function is quadratic, but for other loss functions, novel techniques are required. Second, we design IC algorithms with good performance guarantees for the absolute loss function. Third, we give a formal separation between the power of online prediction with selfish experts and online prediction with honest experts by proving lower bounds for both IC and non-IC algorithms. In particular, with selfish experts and the absolute loss function, there is no (randomized) algorithm for online prediction-IC or otherwise-with asymptotically vanishing regret.

Connected through hyperedge HE-1 (induced by source paper "no regret and incentive compatible online learning"):
  with members:
    - "multi period trading prediction markets with connections to machine learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a collaborative mechanism for crowdsourcing prediction problems"

Connected through hyperedge HE-2 (induced by source paper "lie on the fly strategic voting in an iterative preference elicitation process"):
  with members:
    - "cognitive hierarchy and voting manipulation"
    - "a local dominance theory of voting equilibria"
    - "the computational impact of partial votes on strategic voting"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "dominating manipulations in voting with partial information"
    - "equilibria of plurality voting with abstentions"
    - "where are the hard manipulation problems"

Connected through hyperedge HE-3 (induced by source paper "new potential based bounds for prediction with expert advice"):
  with members:
    - "new potential based bounds for the geometric stopping version of prediction with expert advice"
    - "pde approach to the problem of online prediction with expert advice a construction of potential based strategies"
    - "towards optimal algorithms for prediction with expert advice"

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)