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: "complexity of combinatorial market makers"
  Abstract: We analyze the computational complexity of market maker pricing algorithms for combinatorial prediction markets. We focus on Hanson's popular logarithmic market scoring rule market maker (LMSR). Our goal is to implicitly maintain correct LMSR prices across an exponentially large outcome space. We examine both permutation combinatorics, where outcomes are permutations of objects, and Boolean combinatorics, where outcomes are combinations of binary events. We look at three restrictive languages that limit what traders can bet on. Even with severely limited languages, we find that LMSR pricing is $\SP$-hard, even when the same language admits polynomial-time matching without the market maker. We then propose an approximation technique for pricing permutation markets based on a recent algorithm for online permutation learning. The connections we draw between LMSR pricing and the vast literature on online learning with expert advice may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "how to calculate partition functions using convex programming hierarchies provable bounds for variational methods"):
  with members:
    - "rounding sum of squares relaxations"
    - "convexifying the bethe free energy"
    - "a new class of upper bounds on the log partition function"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximate counting via correlation decay in spin systems"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (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"
    - "an optimization based framework for automated market making"

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"
    - "an optimization based framework for automated market making"

Connected through hyperedge HE-4 (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"
    - "an optimization based framework for automated market making"

Connected through hyperedge HE-5 (induced by source paper "price updating in combinatorial prediction markets with bayesian networks"):
  with members:
    - "aggregating learned probabilistic beliefs"
    - "a utility framework for bounded loss market makers"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-6 (induced by source paper "probability and asset updating using bayesian networks for combinatorial prediction markets"):
  with members:
    - "lazy propagation in junction trees"
    - "price updating in combinatorial prediction markets with bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "an optimization based framework for automated market making"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "a new understanding of prediction markets via no regret learning"
    - "parimutuel betting on permutations"

Connected through hyperedge HE-8 (induced by source paper "a new understanding of prediction markets via no regret learning"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "adaptive online prediction by following the perturbed leader"

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)