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: "a complexity view of markets with social influence"
  Abstract: In this paper, inspired by the work of Megiddo on the formation of preferences and strategic analysis, we consider an early market model studied in the field of economic theory, in which each trader's utility may be influenced by the bundles of goods obtained by her social neighbors. The goal of this paper is to understand and characterize the impact of social influence on the complexity of computing and approximating market equilibria. #R##N#We present complexity-theoretic and algorithmic results for approximating market equilibria in this model with focus on two concrete influence models based on the traditional linear utility functions. Recall that an Arrow-Debreu market equilibrium in a conventional exchange market with linear utility functions can be computed in polynomial time by convex programming. Our complexity results show that even a bounded-degree, planar influence network can significantly increase the difficulty of equilibrium computation even in markets with only a constant number of goods. Our algorithmic results suggest that finding an approximate equilibrium in markets with hierarchical influence networks might be easier than that in markets with arbitrary neighborhood structures. By demonstrating a simple market with a constant number of goods and a bounded-degree, planar influence graph whose equilibrium is PPAD-hard to approximate, we also provide a counterexample to a common belief, which we refer to as the myth of a constant number of goods, that equilibria in markets with a constant number of goods are easy to compute or easy to approximate.

Connected through hyperedge HE-1 (induced by source paper "the complexity of fairness through equilibrium"):
  with members:
    - "inapproximability of nash equilibrium"
    - "the complexity of non monotone markets"
    - "spending is not easier than trading on the computational equivalence of fisher and arrow debreu equilibria"
    - "reducibility among fractional stability problems"
    - "settling the complexity of computing two player nash equilibria"
    - "on the approximation and smoothed complexity of leontief market equilibria"

Connected through hyperedge HE-2 (induced by source paper "the complexity of non monotone markets"):
  with members:
    - "leontief exchange markets can solve multivariate polynomial equations yielding fixp and etr hardness"
    - "inapproximability of nash equilibrium"
    - "settling the complexity of arrow debreu equilibria in markets with additively separable utilities"
    - "spending is not easier than trading on the computational equivalence of fisher and arrow debreu equilibria"
    - "paths beyond local search a nearly tight bound for randomized fixed point computation"
    - "settling the complexity of computing two player nash equilibria"
    - "on the approximation and smoothed complexity of leontief market equilibria"

Connected through hyperedge HE-3 (induced by source paper "optimal pricing in social networks with incomplete information"):
  with members:
    - "pricing strategies for viral marketing on social networks"
    - "settling the complexity of computing two player nash equilibria"

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)