human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "paths beyond local search a nearly tight bound for randomized fixed point computation"):
  Abstract: In 1983, Aldous proved that randomization can speedup local search. For example, it reduces the query complexity of local search over [1:n]^d from Theta (n^{d-1}) to O (d^{1/2}n^{d/2}). It remains open whether randomization helps fixed-point computation. Inspired by this open problem and recent advances on equilibrium computation, we have been fascinated by the following question: #R##N#Is a fixed-point or an equilibrium fundamentally harder to find than a local optimum? In this paper, we give a nearly-tight bound of Omega(n)^{d-1} on the randomized query complexity for computing a fixed point of a discrete Brouwer function over [1:n]^d. Since the randomized query complexity of global optimization over [1:n]^d is Theta (n^{d}), the randomized query model over [1:n]^d strictly separates these three important search problems: Global optimization is harder than fixed-point computation, and fixed-point computation is harder than local search. Our result indeed demonstrates that randomization does not help much in fixed-point computation in the query model; the deterministic complexity of this problem is Theta (n^{d-1}).

Includes members:
  Member "on the approximation and smoothed complexity of leontief market equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complexity of fairness through equilibrium")
  Member "computing nash equilibria approximation and smoothed complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improper learning for non stochastic control")

Question: Please tell me which class the source paper 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)