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: "budget feasible mechanism design from prior free to bayesian"
  Abstract: Budget feasible mechanism design studies procurement combinatorial auctions in which the sellers have private costs to produce items, and the buyer (auctioneer) aims to maximize a social valuation function on subsets of items, under the budget constraint on the total payment. One of the most important questions in the field is "which valuation domains admit truthful budget feasible mechanisms with 'small' approximations (compared to the social optimum)?" Singer [35] showed that additive and submodular functions have a constant approximation mechanism. Recently, Dobzinski, Papadimitriou, and Singer [20] gave an O(log2n) approximation mechanism for subadditive functions; further, they remarked that: "A fundamental question is whether, regardless of computational constraints, a constant-factor budget feasible mechanism exists for subadditive functions."   In this paper, we address this question from two viewpoints: prior-free worst case analysis and Bayesian analysis, which are two standard approaches from computer science and economics, respectively. - For the prior-free framework, we use a linear program (LP) that describes the fractional cover of the valuation function; the LP is also connected to the concept of approximate core in cooperative game theory. We provide a mechanism for subadditive functions whose approximation is O(I), via the worst case integrality gap I of this LP. This implies an O(log n)-approximation for subadditive valuations, O(1)-approximation for XOS valuations, as well as for valuations having a constant integrality gap. XOS valuations are an important class of functions and lie between the submodular and the subadditive classes of valuations. We further give another polynomial time O(log n/(log log n)) sub-logarithmic approximation mechanism for subadditive functions. Both of our mechanisms improve the best known approximation ratio O(log2 n). - For the Bayesian framework, we provide a constant approximation mechanism for all subadditive functions, using the above prior-free mechanism for XOS valuations as a subroutine. Our mechanism allows correlations in the distribution of private information and is universally truthful.

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"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a simple mechanism for a budget constrained buyer"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "revenue maximization and ex post budget constraints"
    - "the menu size complexity of revenue approximation"
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "mixture selection mechanism design and signaling"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-3 (induced by source paper "a bridge between liquid and social welfare in combinatorial auctions with submodular bidders"):
  with members:
    - "a bayesian clearing mechanism for combinatorial auctions"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "bayesian budget feasibility with posted pricing"
    - "combinatorial auctions via posted prices"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "on the approximability of budget feasible mechanisms"

Connected through hyperedge HE-4 (induced by source paper "on budget feasible mechanism design for symmetric submodular objectives"):
  with members:
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "simple and efficient budget feasible mechanisms for monotone submodular valuations"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

Connected through hyperedge HE-6 (induced by source paper "budget feasible mechanisms on matroids"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "truthful multi unit procurements with budgets"
    - "polyhedral clinching auctions and the adwords polytope"
    - "matroid prophet inequalities"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

Connected through hyperedge HE-7 (induced by source paper "coverage matching and beyond new results on budgeted mechanism design"):
  with members:
    - "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"
    - "budget feasible mechanisms"

Connected through hyperedge HE-8 (induced by source paper "liquid price of anarchy"):
  with members:
    - "on the efficiency of the proportional allocation mechanism for divisible resources"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "improved efficiency guarantees in auctions with budgets"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "truthful multi unit procurements with budgets"
    - "price of anarchy for auction revenue"
    - "welfare guarantees for proportional allocations"
    - "combinatorial walrasian equilibrium"

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)