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: "auctions with online supply"
  Abstract: We study the problem of selling identical goods to n unit-demand bidders in a setting in which the total supply of goods is unknown to the mechanism. Items arrive dynamically, and the seller must make the allocation and payment decisions online with the goal of maximizing social welfare. We consider two models of unknown supply: the adversarial supply model, in which the mechanism must produce a welfare guarantee for any arbitrary supply, and the stochastic supply model, in which supply is drawn from a distribution known to the mechanism, and the mechanism need only provide a welfare guarantee in expectation. #R##N#Our main result is a separation between these two models. We show that all truthful mechanisms, even randomized, achieve a diminishing fraction of the optimal social welfare (namely, no better than a Omega(loglog n) approximation) in the adversarial setting. In sharp contrast, in the stochastic model, under a standard monotone hazard-rate condition, we present a truthful mechanism that achieves a constant approximation. We show that the monotone hazard rate condition is necessary, and also characterize a natural subclass of truthful mechanisms in our setting, the set of online-envy-free mechanisms. All of the mechanisms we present fall into this class, and we prove almost optimal lower bounds for such mechanisms. Since auctions with unknown supply are regularly run in many online-advertising settings, our main results emphasize the importance of considering distributional information in the design of auctions in such environments.

Connected through hyperedge HE-1 (induced by source paper "truthful multi parameter auctions with online supply an impossible combination"):
  with members:
    - "general truthfulness characterizations via convex analysis"
    - "the power of randomness in bayesian optimal mechanism design"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-2 (induced by source paper "multi parameter auctions with online supply"):
  with members:
    - "the power of randomness in bayesian optimal mechanism design"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "welfare maximization with production costs a primal dual approach"):
  with members:
    - "convex program duality fisher markets and nash social welfare"
    - "fast algorithms for online stochastic convex programming"
    - "a rational convex program for linear arrow debreu markets"
    - "limitations of randomized mechanisms for combinatorial auctions"
    - "online primal dual for non linear optimization with applications to speed scaling"
    - "welfare and profit maximization with production costs"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "market equilibrium with transaction costs"

Connected through hyperedge HE-4 (induced by source paper "clinching auctions with online supply"):
  with members:
    - "polyhedral clinching auctions and the adwords polytope"
    - "prior free auctions for budgeted agents"
    - "on multiple round sponsored search auctions with budgets"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "incentive compatible budget elicitation in multi unit auctions"

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)