human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an algorithmic characterization of multi dimensional mechanisms"):
  Abstract: We obtain a characterization of feasible, Bayesian, multi-item multi-bidder auctions with independent, additive bidders as distributions over hierarchical mechanisms. Combined with cyclic-monotonicity our results provide a complete characterization of feasible, Bayesian Incentive Compatible (BIC) auctions for this setting. #R##N#Our characterization is enabled by a novel, constructive proof of Border's theorem, and a new generalization of this theorem to independent (but not necessarily iid) bidders. For one item and independent bidders, we show that any feasible reduced form auction can be implemented as a distribution over hierarchical mechanisms. We also give a polytime algorithm for determining feasibility of a reduced form, or finding a separation hyperplane from feasible reduced forms. Finally, we provide polytime algorithms to find and exactly sample from a distribution over hierarchical mechanisms consistent with a given feasible reduced form. #R##N#Our results generalize to multi-item reduced forms for independent, additive bidders. For multiple items, additive bidders with hard demand constraints, and arbitrary value correlation across items or bidders, we give a proper generalization of Border's theorem, and characterize feasible reduced forms as multicommodity flows in related multicommodity flow instances. We show that our generalization holds for a broader class of feasibility constraints, including intersections of any two matroids. #R##N#As a corollary we obtain revenue-optimal, BIC mechanisms in multi-item multi-bidder settings, when each bidder has arbitrarily correlated values over the items and additive valuations over bundles, and bidders are independent. Their runtime is polynomial in the total number of bidder types (instead of type profiles), and is improved to poly(#items, #bidders) using recent structural results on optimal BIC auctions in item-symmetric settings.

Includes members:
  Member "bayesian optimal auctions via multi to single agent reduction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "simple and nearly optimal multi item auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations")
  Member "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "extreme value theorems for optimal multidimensional pricing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer")
  Member "on optimal multi dimensional mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations")
  Member "the power of randomness in bayesian optimal mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "optimal auctions with correlated bidders are easy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lp based approximation for personalized reserve prices")
  Member "bayesian incentive compatibility via fractional assignments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an efficient e bic to bic transformation and its application to black box reduction in revenue maximization")

Question: Please tell me which class the source paper 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)