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 pricing is hard"
  Abstract: We show that computing the revenue-optimal deterministic auction in unit-demand single-buyer Bayesian settings, i.e. the optimal item-pricing, is computationally hard even in single-item settings where the buyer's value distribution is a sum of independently distributed attributes, or multi-item settings where the buyer's values for the items are independent. We also show that it is intractable to optimally price the grand bundle of multiple items for an additive bidder whose values for the items are independent. These difficulties stem from implicit definitions of a value distribution. We provide three instances of how different properties of implicit distributions can lead to intractability: the first is a #P-hardness proof, while the remaining two are reductions from the SQRT-SUM problem of Garey, Graham, and Johnson. While simple pricing schemes can oftentimes approximate the best scheme in revenue, they can have drastically different underlying structure. We argue therefore that either the specification of the input distribution must be highly restricted in format, or it is necessary for the goal to be mere approximation to the optimal scheme's revenue instead of computing properties of the scheme itself.

Connected through hyperedge HE-1 (induced by source paper "on the complexity of bundle pricing and simple mechanisms"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "strong duality for a multiple good monopolist"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "the complexity of optimal multidimensional pricing"
    - "optimal mechanisms with simple menus"
    - "the menu size complexity of auctions"
    - "the complexity of optimal mechanism design"
    - "simple and nearly optimal multi item auctions"

Connected through hyperedge HE-2 (induced by source paper "mechanism design for subadditive agents via an ex ante relaxation"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "online contention resolution schemes"
    - "selling two goods optimally"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "duality and optimality of auctions for uniform distributions"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "strong duality for a multiple good monopolist"

Connected through hyperedge HE-3 (induced by source paper "the complexity of optimal multidimensional pricing"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "the complexity of optimal mechanism design"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "an algorithmic characterization of multi dimensional mechanisms"
    - "extreme value theorems for optimal multidimensional pricing"
    - "the power of randomness in bayesian optimal mechanism design"
    - "optimal auctions with correlated bidders are easy"

Connected through hyperedge HE-4 (induced by source paper "the complexity of optimal mechanism design"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "reducing revenue to welfare maximization approximation algorithms and other generalizations"
    - "mechanism design via optimal transport"
    - "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"
    - "matroid prophet inequalities"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"

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)