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: "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
  Abstract: We study the revenue maximization problem of a seller with n heterogeneous items for sale to a single buyer whose valuation function for sets of items is unknown and drawn from some distribution D. We show that if D is a distribution over subadditive valuations with independent items, then the better of pricing each item separately or pricing only the grand bundle achieves a constant-factor approximation to the revenue of the optimal mechanism. This includes buyers who are k-demand, additive up to a matroid constraint, or additive up to constraints of any downwards-closed set system (and whose values for the individual items are sampled independently), as well as buyers who are fractionally subadditive with item multipliers drawn independently. Our proof makes use of the core-tail decomposition framework developed in prior work showing similar results for the significantly simpler class of additive buyers [LY13, BILW14]. #R##N#In the second part of the paper, we develop a connection between approximately optimal simple mechanisms and approximate revenue monotonicity with respect to buyers' valuations. Revenue non-monotonicity is the phenomenon that sometimes strictly increasing buyers' values for every set can strictly decrease the revenue of the optimal mechanism [HR12]. Using our main result, we derive a bound on how bad this degradation can be (and dub such a bound a proof of approximate revenue monotonicity); we further show that better bounds on approximate monotonicity imply a better analysis of our simple mechanisms.

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 credible and approximately optimal auctions"):
  with members:
    - "an improved welfare guarantee for first price auctions"
    - "learning multi item auctions with or without samples"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "sample complexity for non truthful mechanisms"
    - "the price of anarchy in auctions"
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"

Connected through hyperedge HE-3 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer"):
  with members:
    - "beyond worst case analysis"
    - "simple versus optimal contracts"
    - "approximate revenue maximization with multiple items"
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "strong duality for a multiple good monopolist"
    - "the complexity of optimal mechanism design"

Connected through hyperedge HE-4 (induced by source paper "learning reserve prices in second price auctions"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "tight revenue gaps among simple mechanisms"
    - "tight approximation ratio of anonymous pricing"
    - "a general theory of sample complexity for multi item profit maximization"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "simple mechanisms for profit maximization in multi item auctions"):
  with members:
    - "the best of both worlds asymptotically efficient mechanisms with a guarantee on the expected gains from trade"
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "almost efficient mechanisms for bilateral trading"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "approximately efficient two sided combinatorial auctions"

Connected through hyperedge HE-7 (induced by source paper "approximation schemes for a unit demand buyer with independent items via symmetries"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "optimal deterministic mechanisms for an additive buyer"
    - "approximate revenue maximization with multiple items"
    - "the menu complexity of one and a half dimensional mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "settling the complexity of computing approximate two player nash equilibria"

Connected through hyperedge HE-8 (induced by source paper "robust revenue maximization under minimal statistical information"):
  with members:
    - "revenue maximization with imprecise distribution"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bayesian auctions with efficient queries"
    - "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"

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)