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: "bernoulli factories and black box reductions in mechanism design"
  Abstract: We provide a polynomial time reduction from Bayesian incentive compatible mechanism design to Bayesian algorithm design for welfare maximization problems. Unlike prior results, our reduction achieves exact incentive compatibility for problems with multi-dimensional and continuous type spaces. The key technical barrier preventing exact incentive compatibility in prior black-box reductions is that repairing violations of incentive constraints requires understanding the distribution of the mechanism's output. Reductions that instead estimate the output distribution by sampling inevitably suffer from sampling error, which typically precludes exact incentive compatibility. #R##N#We overcome this barrier by employing and generalizing the computational model in the literature on Bernoulli Factories. In a Bernoulli factory problem, one is given a function mapping the bias of an "input coin" to that of an "output coin", and the challenge is to efficiently simulate the output coin given sample access to the input coin. We generalize this to the "expectations from samples" computational model, in which an instance is specified by a function mapping the expected values of a set of input distributions to a distribution over outcomes. The challenge is to give a polynomial time algorithm that exactly samples from the distribution over outcomes given only sample access to the input distributions. In this model, we give a polynomial time algorithm for the exponential weights: expected values of the input distributions correspond to the weights of alternatives and we wish to select an alternative with probability proportional to an exponential function of its weight. This algorithm is the key ingredient in designing an incentive compatible mechanism for bipartite matching, which can be used to make the approximately incentive compatible reduction of Hartline et al. (2015) exactly incentive compatible.

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"
    - "the menu size complexity of revenue approximation"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "non optimal mechanism design"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-2 (induced by source paper "an efficient e bic to bic transformation and its application to black box reduction in revenue maximization"):
  with members:
    - "multi item mechanisms without item independence learnability via robustness"
    - "the complexity of black box mechanism design with priors"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

Connected through hyperedge HE-3 (induced by source paper "the complexity of black box mechanism design with priors"):
  with members:
    - "on black box transformations in downward closed environments"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "non optimal mechanism design"
    - "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-4 (induced by source paper "the sample complexity of up to varepsilon multi dimensional revenue maximization"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "learning multi item auctions with or without samples"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"
    - "efficient empirical revenue maximization in single parameter auction environments"

Connected through hyperedge HE-5 (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"
    - "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)