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: "capacity as a fundamental metric for mechanism design in the information economy"
  Abstract: Department of Electrical Engineering, University of California, Los Angeles, CA 90095.{sudhir,vwani} @ ee.ucla.eduAbstract. The auction theory literature has so far focused mostly on the designof mechanisms that takes the revenue or the efﬁciency as a yar dstick. However,scenarios where the capacity, which we deﬁne as “the number of bidders theauctioneer wants to have a positive probability of getting the item” , is a fun-damental concern are ubiquitous in the information economy. For instance, insponsored search auctions (SSA’s) or in online ad-exchanges, the true value ofan ad-slot for an advertiser is inherently derived from the conversion-rate, whichin turn depends on whether the advertiser actually obtained the ad-slot or not;thus, unless the capacity of the underlying auction is large, key parameters, suchas true valuations and advertiser-speciﬁc conversion rates, will remain unknownor uncertain leading to inherent inefﬁciencies in the syste m. In general, the sameholds true for all information goods/digital goods. We initiate a study of mecha-nisms, which take capacity as a yardstick, in addition to revenue/efﬁciency. Weshow that in the case of a single indivisible item one simple way to incorporatecapacity constraints is via designing mechanisms to sell probability distributions,and that under certain conditions, such optimal probability distributions couldbe identiﬁed using a Linear programming approach. We deﬁne a quantity calledprice of capacity to capture the tradeoff between capacity and revenue/efﬁci ency.We also study the case of sponsored search auctions. Finally, we discuss how gen-eral such an approach via probability spikes can be made, and potential directionsfor future investigations.

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)