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: "optimal auctions via the multiplicative weight method"
  Abstract: We show that the multiplicative weight update method provides a simple recipe for designing and analyzing optimal Bayesian Incentive Compatible (BIC) auctions, and reduces the time complexity of the problem to pseudo-polynomial in parameters that depend on single agent instead of depending on the size of the joint type space. We use this framework to design computationally efficient optimal auctions that satisfy ex-post Individual Rationality in the presence of constraints such as (hard, private) budgets and envy-freeness. We also design optimal auctions when buyers and a seller's utility functions are non-linear. Scenarios with such functions include (a) auctions with "quitting rights", (b) cost to borrow money beyond budget, (c) a seller's and buyers' risk aversion. Finally, we show how our framework also yields optimal auctions for variety of auction settings considered in Cai et al, Alaei et al, albeit with pseudo-polynomial running times.

Connected through hyperedge HE-1 (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"
    - "bernoulli factories and black box reductions in 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"

Connected through hyperedge HE-2 (induced by source paper "a duality based unified approach to bayesian mechanism design"):
  with members:
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "the value of information concealment"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "mechanism design for subadditive agents via an ex ante relaxation"

Connected through hyperedge HE-3 (induced by source paper "learning multi item auctions with or without samples"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-4 (induced by source paper "sample complexity of automated mechanism design"):
  with members:
    - "learning simple auctions"
    - "do prices coordinate markets"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "complexity of mechanism design"

Connected through hyperedge HE-5 (induced by source paper "revenue maximization and ex post budget constraints"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "revenue maximization for selling multiple correlated items"
    - "bayesian truthful mechanisms for job scheduling from bi criterion approximation algorithms"
    - "understanding incentives mechanism design becomes algorithm design"

Connected through hyperedge HE-6 (induced by source paper "robus fair cache allocation for multi tenant data parallel workloads"):
  with members:
    - "competitive algorithms from competitive equilibria non clairvoyant scheduling under polyhedral constraints"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "restore reusing results of mapreduce jobs"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-7 (induced by source paper "a simple and approximately optimal mechanism for an additive buyer"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "reducing revenue to welfare maximization approximation algorithms and other generalizations"
    - "understanding incentives mechanism design becomes algorithm design"
    - "optimal mechanisms with simple menus"
    - "the menu size complexity of auctions"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"

Connected through hyperedge HE-8 (induced by source paper "revenue maximization for selling multiple correlated items"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "simple mechanisms for a combinatorial buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "reducing revenue to welfare maximization approximation algorithms and other generalizations"
    - "the menu size complexity of auctions"
    - "bayesian optimal auctions via multi to single agent reduction"

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)