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: "the simple economics of approximately optimal auctions"
  Abstract: The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in microeconomics. In the classical auction theory for agents with linear utility and single-dimensional preferences, Bulow and Roberts (1989) show that the optimal auction of Myerson (1981) is in fact optimizing marginal revenue. In particular Myerson's virtual values are exactly the derivative of an appropriate revenue curve. #R##N#This paper considers mechanism design in environments where the agents have multi-dimensional and non-linear preferences. Understanding good auctions for these environments is considered to be the main challenge in Bayesian optimal mechanism design. In these environments maximizing marginal revenue may not be optimal and there is sometimes no direct way to implement the marginal revenue maximization. Our contributions are three fold: we characterize the settings for which marginal revenue maximization is optimal (by identifying an important condition that we call revenue linearity), we give simple procedures for implementing marginal revenue maximization in general, and we show that marginal revenue maximization is approximately optimal. Our approximation factor smoothly degrades in a term that quantifies how far the environment is from ideal (where marginal revenue maximization is optimal). Because the marginal revenue mechanism is optimal for single-dimensional agents, our generalization immediately approximately extends many results for single-dimensional agents. #R##N#One of the biggest open questions in Bayesian algorithmic mechanism design is developing methodologies that are not brute-force in the size of the agent type space. Our methods identify a subproblem that, e.g., for unit-demand agents with values drawn from product distributions, enables approximation mechanisms that are polynomial in the dimension.

Connected through hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "a neural architecture for designing truthful and efficient auctions"
    - "multi agent adversarial inverse reinforcement learning"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "tight revenue gaps among simple mechanisms"
    - "a duality based unified approach to bayesian mechanism design"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-2 (induced by source paper "simple mechanisms for non linear agents"):
  with members:
    - "optimal auctions vs anonymous pricing beyond linear utility"
    - "optimal online contention resolution schemes via ex ante prophet inequalities"
    - "a simple mechanism for a budget constrained buyer"
    - "online contention resolution schemes"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "mechanism design via correlation gap"
    - "correlation robust stochastic optimization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "optimal auctions vs anonymous pricing beyond linear utility"):
  with members:
    - "tight revenue gaps among simple mechanisms"
    - "tight approximation ratio of anonymous pricing"
    - "optimal auctions vs anonymous pricing"
    - "prior independent auctions for risk averse agents"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "sequential posted pricing and multi parameter mechanism design"
    - "mechanism design via correlation gap"

Connected through hyperedge HE-5 (induced by source paper "tight revenue gaps among simple mechanisms"):
  with members:
    - "prophet secretary through blind strategies"
    - "a duality based unified approach to bayesian mechanism design"
    - "nearly optimal pricing algorithms for production constrained and laminar bayesian selection"
    - "improved approximations for free order prophets and second price auctions"
    - "tight approximation ratio of anonymous pricing"
    - "approximate revenue maximization with multiple items"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "prophet secretary for combinatorial auctions and matroids"

Connected through hyperedge HE-6 (induced by source paper "tight approximation ratio of anonymous pricing"):
  with members:
    - "tight revenue gaps among simple mechanisms"
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue gaps for discriminatory and anonymous sequential posted pricing"
    - "revenue gaps for static and dynamic posted pricing of homogeneous goods"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-7 (induced by source paper "an end to end argument in mechanism design prior independent auctions for budgeted agents"):
  with members:
    - "a b testing of auctions"
    - "the price of anarchy in auctions"
    - "randomization beats second price as a prior independent auction"
    - "approximate revenue maximization in interdependent value settings"
    - "mechanism design for data science"
    - "simple and near optimal mechanisms for market intermediation"
    - "price of anarchy for auction revenue"
    - "efficiency guarantees in auctions with budgets"

Connected through hyperedge HE-8 (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"

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)