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: "simple and efficient budget feasible mechanisms for monotone submodular valuations"
  Abstract: We study the problem of a budget limited buyer who wants to buy a set of items, each from a different seller, to maximize her value. The budget feasible mechanism design problem aims to design a mechanism which incentivizes the sellers to truthfully report their cost, and maximizes the buyer's value while guaranteeing that the total payment does not exceed her budget. Such budget feasible mechanisms can model a buyer in a crowdsourcing market interested in recruiting a set of workers (sellers) to accomplish a task for her. #R##N#This budget feasible mechanism design problem was introduced by Singer in 2010. There have been a number of improvements on the approximation guarantee of such mechanisms since then. We consider the general case where the buyer's valuation is a monotone submodular function. We offer two general frameworks for simple mechanisms, and by combining these frameworks, we significantly improve on the best known results for this problem, while also simplifying the analysis. For example, we improve the approximation guarantee for the general monotone submodular case from 7.91 to 5; and for the case of large markets (where each individual item has negligible value) from 3 to 2.58. More generally, given an $r$ approximation algorithm for the optimization problem (ignoring incentives), our mechanism is a $r+1$ approximation mechanism for large markets, an improvement from $2r^2$. We also provide a similar parameterized mechanism without the large market assumption, where we achieve a $4r+1$ approximation guarantee.

Connected through hyperedge HE-1 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online"):
  with members:
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "bayesian budget feasibility with posted pricing"

Connected through hyperedge HE-2 (induced by source paper "optimal budget feasible mechanisms for additive valuations"):
  with members:
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "budget feasible mechanisms on matroids"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "budget feasible mechanisms for experimental design"
    - "on the approximability of budget feasible mechanisms"
    - "frugal mechanism design via spectral techniques"

Connected through hyperedge HE-3 (induced by source paper "on budget feasible mechanism design for symmetric submodular objectives"):
  with members:
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "budget feasible mechanisms for experimental design"
    - "incentives for privacy tradeoff in community sensing"
    - "budget feasible mechanism design from prior free to bayesian"

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)