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: "applications of alpha strongly regular distributions to bayesian auctions"
  Abstract: Two classes of distributions that are widely used in the analysis of Bayesian auctions are the Monotone Hazard Rate (MHR) and Regular distributions. They can both be characterized in terms of the rate of change of the associated virtual value functions: for MHR distributions the condition is that for values $v < v'$, $\phi(v') - \phi(v) \ge v' - v$, and for regular distributions, $\phi(v') - \phi(v) \ge 0$. Cole and Roughgarden introduced the interpolating class of $\alpha$-Strongly Regular distributions ($\alpha$-SR distributions for short), for which $\phi(v') - \phi(v) \ge \alpha(v' - v)$, for $0 \le \alpha \le 1$. #R##N#In this paper, we investigate five distinct auction settings for which good expected revenue bounds are known when the bidders' valuations are given by MHR distributions. In every case, we show that these bounds degrade gracefully when extended to $\alpha$-SR distributions. For four of these settings, the auction mechanism requires knowledge of these distribution(s) (in the other setting, the distributions are needed only to ensure good bounds on the expected revenue). In these cases we also investigate what happens when the distributions are known only approximately via samples, specifically how to modify the mechanisms so that they remain effective and how the expected revenue depends on the number of samples.

Connected through hyperedge HE-1 (induced by source paper "pricing to maximize revenue and welfare simultaneously in large markets"):
  with members:
    - "do prices coordinate markets"
    - "the sample complexity of revenue maximization"
    - "envy free pricing in large markets approximating revenue and welfare"
    - "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"

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)