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: "on revenue maximization with sharp multi unit demands"
  Abstract: We consider markets consisting of a set of indivisible items, and buyers that have {\em sharp} multi-unit demand. This means that each buyer $i$ wants a specific number $d_i$ of items; a bundle of size less than $d_i$ has no value, while a bundle of size greater than $d_i$ is worth no more than the most valued $d_i$ items (valuations being additive). We consider the objective of setting prices and allocations in order to maximize the total revenue of the market maker. The pricing problem with sharp multi-unit demand buyers has a number of properties that the unit-demand model does not possess, and is an important question in algorithmic pricing. We consider the problem of computing a revenue maximizing solution for two solution concepts: competitive equilibrium and envy-free pricing. #R##N#For unrestricted valuations, these problems are NP-complete; we focus on a realistic special case of "correlated values" where each buyer $i$ has a valuation $v_i\qual_j$ for item $j$, where $v_i$ and $\qual_j$ are positive quantities associated with buyer $i$ and item $j$ respectively. We present a polynomial time algorithm to solve the revenue-maximizing competitive equilibrium problem. For envy-free pricing, if the demand of each buyer is bounded by a constant, a revenue maximizing solution can be found efficiently; the general demand case is shown to be NP-hard.

Connected through hyperedge HE-1 (induced by source paper "pricing to maximize revenue and welfare simultaneously in large markets"):
  with members:
    - "do prices coordinate markets"
    - "the sample complexity of revenue maximization"
    - "envy free pricing in large markets approximating revenue and welfare"
    - "applications of alpha strongly regular distributions to bayesian auctions"
    - "optimising trade offs among stakeholders in ad auctions"
    - "on the ratio of revenue to welfare in single parameter mechanism design"
    - "pricing ad slots with consecutive multi unit demand"
    - "efficiency revenue trade offs in auctions"

Connected through hyperedge HE-2 (induced by source paper "envy free pricing in large markets approximating revenue and welfare"):
  with members:
    - "pricing to maximize revenue and welfare simultaneously in large markets"
    - "price competition in networked markets how do monopolies impact social welfare"
    - "combinatorial auctions via posted prices"
    - "combinatorial walrasian equilibrium"
    - "algorithmic pricing via virtual valuations"

Connected through hyperedge HE-3 (induced by source paper "pricing ad slots with consecutive multi unit demand"):
  with members:
    - "efficiency and complexity of price competition among single product vendors"
    - "combinatorial auctions via posted prices"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "an algorithmic characterization of multi dimensional mechanisms"
    - "bidding for representative allocations for display advertising"
    - "budget constrained auctions with heterogeneous items"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-4 (induced by source paper "approximating the revenue maximization problem with sharp demands"):
  with members:
    - "combinatorial walrasian equilibrium"
    - "independent set induced matching and pricing connections and tight subexponential time approximation hardnesses"
    - "pricing ad slots with consecutive multi unit demand"
    - "graph products revisited tight approximation hardness of induced matching poset dimension and more"

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)