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 utility framework for bounded loss market makers"
  Abstract: We introduce a class of utility-based market makers that always accept orders at their risk-neutral prices. We derive necessary and sufficient conditions for such market makers to have bounded loss. We prove that hyperbolic absolute risk aversion utility market makers are equivalent to weighted pseudospherical scoring rule market makers. In particular, Hanson's logarithmic scoring rule market maker corresponds to a negative exponential utility market maker in our framework. We describe a third equivalent formulation based on maintaining a cost function that seems most natural for implementation purposes, and we illustrate how to translate among the three equivalent formulations. We examine the tradeoff between the market's liquidity and the market maker's worst-case loss. For a fixed bound on worst-case loss, some market makers exhibit greater liquidity near uniform prices and some exhibit greater liquidity near extreme prices, but no market maker can exhibit uniformly greater liquidity in all regimes. For a fixed minimum liquidity level, we give the lower bound of market maker's worst-case loss under some regularity conditions.

Connected through hyperedge HE-1 (induced by source paper "information gathering with peers submodular optimization with peer prediction constraints"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "a framework for designing information elicitation mechanisms that reward truth telling"
    - "learning to incentivize eliciting effort via output agreement"
    - "crowd access path optimization diversity matters"
    - "information gathering in networks via active exploration"
    - "incentives for privacy tradeoff in community sensing"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-2 (induced by source paper "a decomposition of forecast error in prediction markets"):
  with members:
    - "information aggregation in exponential family markets"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-3 (induced by source paper "the possibilities and limitations of private prediction markets"):
  with members:
    - "private matchings and allocations"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-4 (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 new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-5 (induced by source paper "arbitrage free combinatorial market making via integer programming"):
  with members:
    - "market making with decreasing utility for information"
    - "an optimization based framework for automated market making"
    - "complexity of combinatorial market makers"

Connected through hyperedge HE-6 (induced by source paper "budget constraints in prediction markets"):
  with members:
    - "learning performance of prediction markets with kelly bettors"
    - "a new understanding of prediction markets via no regret learning"
    - "an optimization based framework for automated market making"

Connected through hyperedge HE-7 (induced by source paper "multi period trading prediction markets with connections to machine learning"):
  with members:
    - "toward a market model for bayesian inference"
    - "isoelastic agents and wealth updates in machine learning markets"
    - "machine learning markets"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-8 (induced by source paper "market making with decreasing utility for information"):
  with members:
    - "price updating in combinatorial prediction markets with bayesian networks"
    - "an optimization based framework for automated market making"
    - "complexity of combinatorial market makers"

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)