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: "non optimal mechanism design"
  Abstract: The optimal allocation of resources in complex environments—like allocation of dynamic wireless spectrum, cloud computing services, and Internet advertising—is computationally challenging even given the true preferences of the participants. In the theory and practice of optimization in complex environments, a wide variety of special and general purpose algorithms have been developed; these algorithms produce outcomes that are satisfactory but not generally optimal or incentive compatible. This paper develops a very simple approach for converting any, potentially non-optimal, algorithm for optimization given the true participant preferences, into a Bayesian incentive compatible mechanism that weakly improves social welfare and revenue. (JEL D82, H82, L82)

Connected through hyperedge HE-1 (induced by source paper "the complexity of contracts"):
  with members:
    - "delegated search approximates efficient search"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bernoulli factories and black box reductions in mechanism design"
    - "the menu size complexity of revenue approximation"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-2 (induced by source paper "the complexity of black box mechanism design with priors"):
  with members:
    - "on black box transformations in downward closed environments"
    - "bernoulli factories and black box reductions in mechanism design"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "reducing revenue to welfare maximization approximation algorithms and other generalizations"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "bayesian incentive compatibility via fractional assignments"
    - "on the power of randomization in algorithmic mechanism design"

Connected through hyperedge HE-3 (induced by source paper "bernoulli factories and black box reductions in mechanism design"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "fast algorithms for online stochastic convex programming"
    - "a near optimal dynamic learning algorithm for online matching problems with concave returns"
    - "bandits with knapsacks"
    - "multi parameter mechanisms with implicit payment computation"
    - "primal beats dual on online packing lps in the random order model"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "limitations of randomized mechanisms for combinatorial auctions"

Connected through hyperedge HE-4 (induced by source paper "sample complexity for non truthful mechanisms"):
  with members:
    - "an end to end argument in mechanism design prior independent auctions for budgeted agents"
    - "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"
    - "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)