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: "an end to end argument in mechanism design prior independent auctions for budgeted agents"
  Abstract: This paper considers prior-independent mechanism design, namely identifying a single mechanism that has near optimal performance on every prior distribution. We show that mechanisms with truthtelling equilibria, a.k.a., revelation mechanisms, do not always give optimal prior-independent mechanisms and we define the revelation gap to quantify the non-optimality of revelation mechanisms. This study suggests that it is important to develop a theory for the design of non-revelation mechanisms. #R##N#Our analysis focuses on single-item auctions for agents with budgets and the objectives of welfare and with a natural regularity assumption on the distribution. Our main results show that the all-pay auction (a non-revelation mechanism) is an optimal prior-independent approximation mechanism. The all-pay auction, however, is not a revelation mechanism. We prove that prior-independent approximation of best revelation mechanism is strictly worse than that of the all-pay auction. Thus, the revelation gap is at least a constant. Finally, we show that the clinching auction (a revelation mechanism) is a prior-independent $e\ \approx\ 2.714$ approximation. Thus the revelation gap is a constant. #R##N#Many of our analyses extend to the revenue objective, position environments, and irregular distributions.

Connected through hyperedge HE-1 (induced by source paper "benchmark design and prior independent optimization"):
  with members:
    - "randomization beats second price as a prior independent auction"
    - "optimal auctions vs anonymous pricing"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "optimal competitive auctions"
    - "optimal platform design"
    - "envy freedom and prior free mechanism design"
    - "optimal mechansim design and money burning"
    - "a comparison of performance measures for online algorithms"

Connected through hyperedge HE-2 (induced by source paper "sample complexity for non truthful mechanisms"):
  with members:
    - "bernoulli factories and black box reductions in mechanism design"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "the price of anarchy in auctions"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "non optimal mechanism design"
    - "mechanism design for data science"

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)