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: "approximate revenue maximization with multiple items"
  Abstract: Maximizing the revenue from selling more than one good (or item) to a single buyer is a notoriously difficult problem, in stark contrast to the one-good case. For two goods, we show that simple “one-dimensional” mechanisms, such as selling the goods separately, guarantee at least73% of the optimal revenue when the valuations of the two goods are independent and identically distributed, and at least 50% when they are independent.

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 "escaping cannibalization correlation robust pricing for a unit demand buyer"):
  with members:
    - "beyond worst case analysis"
    - "simple versus optimal contracts"
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "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-3 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations"):
  with members:
    - "on optimal mechanisms in the two item single buyer unit demand setting"
    - "a duality based unified approach to bayesian 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"
    - "optimal auctions with restricted allocations"
    - "bounding the optimal revenue of selling multiple goods"
    - "selling two goods optimally"

Connected through hyperedge HE-4 (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"
    - "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"
    - "online contention resolution schemes"

Connected through hyperedge HE-5 (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"
    - "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"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "estimating approximate incentive compatibility"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "optimal auctions through deep learning"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a b testing of auctions"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-8 (induced by source paper "buy many mechanisms are not much better than item pricing"):
  with members:
    - "optimal deterministic mechanisms for an additive buyer"
    - "simple mechanisms for subadditive buyers via duality"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "sampling and representation complexity of revenue maximization"
    - "revenue maximization for selling multiple correlated items"

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)