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 optimization based framework for automated market making"
  Abstract: Building on ideas from online convex optimization, we propose a general framework for the design of efficient securities markets over very large outcome spaces. The challenge here is computational. In a complete market, in which one security is offered for each outcome, the market institution can not efficiently keep track of the transaction history or calculate security prices when the outcome space is large. The natural solution is to restrict the space of securities to be much smaller than the outcome space in such a way that securities can be priced efficiently. Recent research has focused on searching for spaces of securities that can be priced efficiently by existing mechanisms designed for complete markets. While there have been some successes, much of this research has led to hardness results. In this paper, we take a drastically different approach. We start with an arbitrary space of securities with bounded payoff, and establish a framework to design markets tailored to this space. We prove that any market satisfying a set of intuitive conditions must price securities via a convex potential function and that the space of reachable prices must be precisely the convex hull of the security payoffs. We then show how the convex potential function can be defined in terms of an optimization over the convex hull of the security payoffs. The optimal solution to the optimization problem gives the security prices. Using this framework, we provide an efficient market for predicting the landing location of an object on a sphere. In addition, we show that we can relax our "no-arbitrage" condition to design a new efficient market maker for pair betting, which is known to be #P-hard to price using existing mechanisms. This relaxation also allows the market maker to charge transaction fees so that the depth of the market can be dynamically increased as the number of trades increases.

Connected through hyperedge HE-1 (induced by source paper "arbitrage free combinatorial market making via integer programming"):
  with members:
    - "market making with decreasing utility for information"
    - "a utility framework for bounded loss market makers"
    - "complexity of combinatorial market makers"

Connected through hyperedge HE-2 (induced by source paper "budget constraints in prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "learning performance of prediction markets with kelly bettors"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-3 (induced by source paper "market making with decreasing utility for information"):
  with members:
    - "price updating in combinatorial prediction markets with bayesian networks"
    - "a utility framework for bounded loss market makers"
    - "complexity of combinatorial market makers"

Connected through hyperedge HE-4 (induced by source paper "on manipulation in prediction markets when participants influence outcomes directly"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "eliciting forecasts from self interested experts scoring rules for decision makers"

Connected through hyperedge HE-5 (induced by source paper "designing informative securities"):
  with members:
    - "representing aggregate belief through the competitive equilibrium of a securities market"
    - "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"
    - "complexity of combinatorial market makers"

Connected through hyperedge HE-6 (induced by source paper "strategy proof prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "prediction markets mechanism design and cooperative game theory"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-7 (induced by source paper "a collaborative mechanism for crowdsourcing prediction problems"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "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.GT(Computer Science and Game Theory)