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 in social networks with incomplete information"
  Abstract: In revenue maximization of selling a digital product in a social network, the utility of an agent is often considered to have two parts: a private valuation, and linearly additive influences from other agents. We study the incomplete information case where agents know a common distribution about others' private valuations, and make decisions simultaneously. The "rational behavior" of agents in this case is captured by the well-known Bayesian Nash equilibrium. #R##N#Two challenging questions arise: how to compute an equilibrium and how to optimize a pricing strategy accordingly to maximize the revenue assuming agents follow the equilibrium? In this paper, we mainly focus on the natural model where the private valuation of each agent is sampled from a uniform distribution, which turns out to be already challenging. #R##N#Our main result is a polynomial-time algorithm that can exactly compute the equilibrium and the optimal price, when pairwise influences are non-negative. If negative influences are allowed, computing any equilibrium even approximately is PPAD-hard. Our algorithm can also be used to design an FPTAS for optimizing discriminative price profile.

Connected through hyperedge HE-1 (induced by source paper "a stackelberg game approach towards socially aware incentive mechanisms for mobile crowdsensing online report"):
  with members:
    - "a socially aware incentive mechanism for mobile crowdsensing service market"
    - "dynamic routing for social information sharing"
    - "crowdsourcing with tullock contests a new perspective"
    - "optimal pricing in networks with externalities"

Connected through hyperedge HE-2 (induced by source paper "pricing in social networks with negative externalities"):
  with members:
    - "pricing public goods for private sale"
    - "optimal pricing in networks with externalities"

Connected through hyperedge HE-3 (induced by source paper "profit maximization over social networks"):
  with members:
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-4 (induced by source paper "competitive auctions for markets with positive externalities"):
  with members:
    - "on random sampling auctions for digital goods"
    - "pricing strategies for viral marketing on social networks"
    - "sponsored search auctions with markovian users"

Connected through hyperedge HE-5 (induced by source paper "on the efficiency of influence and exploit strategies for revenue maximization under positive externalities"):
  with members:
    - "pricing strategies for viral marketing on social networks"

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)