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: "dynamic and non uniform pricing strategies for revenue maximization"
  Abstract: We consider the Item Pricing problem for revenue maximization in the limited supply setting, where a single seller with $n$ items caters to $m$ buyers with unknown subadditive valuation functions who arrive in a sequence. The seller sets the prices on individual items. Each buyer buys a subset of yet unsold items that maximizes her utility. Our goal is to design pricing strategies that guarantee an expected revenue that is within a small factor $\alpha$ of the maximum possible social welfare -- an upper bound on the maximum revenue that can be generated. Most earlier work has focused on the unlimited supply setting, where selling items to some buyer does not affect their availability to the future buyers. Balcan et. al. (EC 2008) studied the limited supply setting, giving a randomized strategy that assigns a single price to all items (uniform strategy), and never changes it (static strategy), that gives an $2^{O(\sqrt{\log n \log \log n})}$-approximation, and moreover, no static uniform pricing strategy can give better than $2^{\Omega(\log^{1/4} n)}$- approximation. We improve this lower bound to $2^{\Omega(sqrt{\log n})}$. #R##N#We consider dynamic uniform strategies, which can change the price upon the arrival of each buyer but the price on all unsold items is the same at all times, and static non-uniform strategies, which can assign different prices to different items but can never change it after setting it initially. We design such pricing strategies that give a poly-logarithmic approximation to maximum revenue. Thus in the limited supply setting, our results highlight a strong separation between the power of dynamic and non-uniform pricing versus static uniform pricing. To our knowledge, this is the first non-trivial analysis of dynamic and non-uniform pricing schemes for revenue maximization.

Connected through hyperedge HE-1 (induced by source paper "multidimensional dynamic pricing for welfare maximization"):
  with members:
    - "do prices coordinate markets"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "combinatorial auctions via posted prices"
    - "bandits with knapsacks"
    - "efficiently learning from revealed preference"

Connected through hyperedge HE-2 (induced by source paper "posted pricing sans discrimination"):
  with members:
    - "do prices coordinate markets"
    - "on the computational complexity of optimal simple mechanisms"
    - "on welfare approximation and stable pricing"
    - "the invisible hand of dynamic market pricing"
    - "sequential posted price mechanisms with correlated valuations"
    - "welfare maximization with production costs a primal dual approach"
    - "combinatorial auctions via posted prices"
    - "combinatorial walrasian equilibrium"

Connected through hyperedge HE-3 (induced by source paper "welfare maximization with production costs a primal dual approach"):
  with members:
    - "convex program duality fisher markets and nash social welfare"
    - "fast algorithms for online stochastic convex programming"
    - "a rational convex program for linear arrow debreu markets"
    - "limitations of randomized mechanisms for combinatorial auctions"
    - "online primal dual for non linear optimization with applications to speed scaling"
    - "welfare and profit maximization with production costs"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "market equilibrium with transaction costs"

Connected through hyperedge HE-4 (induced by source paper "welfare and profit maximization with production costs"):
  with members:
    - "stackelberg network pricing games"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-5 (induced by source paper "approximation schemes for sequential posted pricing in multi unit auctions"):
  with members:
    - "selective call out and real time bidding"
    - "mechanism design via correlation gap"
    - "mechanisms for multi unit auctions"
    - "on the power of randomization in algorithmic mechanism design"
    - "budget constrained auctions with heterogeneous items"

Connected through hyperedge HE-6 (induced by source paper "sequential item pricing for unlimited supply"):
  with members:
    - "stackelberg network pricing games"

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)