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: "do prices coordinate markets"
  Abstract: Walrasian equilibrium prices have a remarkable property: they allow each buyer to purchase a bundle of goods that she finds the most desirable, while guaranteeing that the induced allocation over all buyers will globally maximize social welfare. However, this clean story has two caveats. * First, the prices may induce indifferences. In fact, the minimal equilibrium prices necessarily induce indifferences. Accordingly, buyers may need to coordinate with one another to arrive at a socially optimal outcome---the prices alone are not sufficient to coordinate the market. * Second, although natural procedures converge to Walrasian equilibrium prices on a fixed population, in practice buyers typically observe prices without participating in a price computation process. These prices cannot be perfect Walrasian equilibrium prices, but instead somehow reflect distributional information about the market. To better understand the performance of Walrasian prices when facing these two problems, we give two results. First, we propose a mild genericity condition on valuations under which the minimal Walrasian equilibrium prices induce allocations which result in low over-demand, no matter how the buyers break ties. In fact, under genericity the over-demand of any good can be bounded by 1, which is the best possible at the minimal prices. We demonstrate our results for unit demand valuations and give an extension to matroid based valuations (MBV), conjectured to be equivalent to gross substitute valuations (GS). Second, we use techniques from learning theory to argue that the over-demand and welfare induced by a price vector converge to their expectations uniformly over the class of all price vectors, with respective sample complexity linear and quadratic in the number of goods in the market. These results make no assumption on the form of the valuation functions. These two results imply that under a mild genericity condition, the exact Walrasian equilibrium prices computed in a market are guaranteed to induce both low over-demand and high welfare when used in a new market where agents are sampled independently from the same distribution, whenever the number of agents is larger than the number of commodities in the market.

Connected through hyperedge HE-1 (induced by source paper "on the power and limits of dynamic pricing in combinatorial markets"):
  with members:
    - "a general framework for endowment effects in combinatorial markets"
    - "combinatorial auctions with endowment effect"
    - "max min greedy matching"
    - "pricing for online resource allocation intervals and paths"
    - "pricing multi unit markets"
    - "makespan minimization via posted prices"
    - "price doubling and item halving robust revenue guarantees for item pricing"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"

Connected through hyperedge HE-2 (induced by source paper "learning to screen"):
  with members:
    - "prophet inequalities for independent random variables from an unknown distribution"
    - "prophets and secretaries with overbooking"
    - "a general theory of sample complexity for multi item profit maximization"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "learning simple auctions"
    - "opting into optimal matchings"
    - "how the experts algorithm can help solve lps online"

Connected through hyperedge HE-3 (induced by source paper "a general theory of sample complexity for multi item profit maximization"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "submultiplicative glivenko cantelli and uniform convergence of revenues"
    - "learning multi item auctions with or without samples"
    - "revenue optimization with approximate bid predictions"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-4 (induced by source paper "sample complexity of automated mechanism design"):
  with members:
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "complexity of mechanism design"
    - "combinatorial auctions via posted prices"

Connected through hyperedge HE-5 (induced by source paper "learning simple auctions"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the optimal sample complexity of pac learning"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "learning economic parameters from revealed preferences"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "sampling and representation complexity of revenue maximization"

Connected through hyperedge HE-6 (induced by source paper "multidimensional dynamic pricing for welfare maximization"):
  with members:
    - "bandit convex optimization sqrt t regret in one dimension"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "combinatorial auctions via posted prices"
    - "bandits with knapsacks"
    - "efficiently learning from revealed preference"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "pricing to maximize revenue and welfare simultaneously in large markets"):
  with members:
    - "the sample complexity of revenue maximization"
    - "envy free pricing in large markets approximating revenue and welfare"
    - "applications of alpha strongly regular distributions to bayesian auctions"
    - "optimising trade offs among stakeholders in ad auctions"
    - "on the ratio of revenue to welfare in single parameter mechanism design"
    - "pricing ad slots with consecutive multi unit demand"
    - "efficiency revenue trade offs in auctions"
    - "lower bounds on revenue of approximately optimal auctions"

Connected through hyperedge HE-8 (induced by source paper "posted pricing sans discrimination"):
  with members:
    - "on the computational complexity of optimal simple mechanisms"
    - "on welfare approximation and stable pricing"
    - "the invisible hand of dynamic market pricing"
    - "sequential posted price mechanisms with correlated valuations"
    - "welfare maximization with production costs a primal dual approach"
    - "combinatorial auctions via posted prices"
    - "combinatorial walrasian equilibrium"
    - "welfare and profit maximization with production costs"

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)