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: "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
  Abstract: In this paper we consider a mechanism design problem in the context of large-scale crowdsourcing markets such as Amazon's Mechanical Turk, ClickWorker, CrowdFlower. In these markets, there is a requester who wants to hire workers to accomplish some tasks. Each worker is assumed to give some utility to the requester. Moreover each worker has a minimum cost that he wants to get paid for getting hired. This minimum cost is assumed to be private information of the workers. The question then is - if the requester has a limited budget, how to design a direct revelation mechanism that picks the right set of workers to hire in order to maximize the requester's utility. #R##N#We note that although the previous work has studied this problem, a crucial difference in which we deviate from earlier work is the notion of large-scale markets that we introduce in our model. Without the large market assumption, it is known that no mechanism can achieve an approximation factor better than 0.414 and 0.5 for deterministic and randomized mechanisms respectively (while the best known deterministic and randomized mechanisms achieve an approximation ratio of 0.292 and 0.33 respectively). In this paper, we design a budget-feasible mechanism for large markets that achieves an approximation factor of 1-1/e (i.e. almost 0.63). Our mechanism can be seen as a generalization of an alternate way to look at the proportional share mechanism which is used in all the previous works so far on this problem. Interestingly, we also show that our mechanism is optimal by showing that no truthful mechanism can achieve a factor better than 1-1/e; thus, fully resolving this setting. Finally we consider the more general case of submodular utility functions and give new and improved mechanisms for the case when the markets are large.

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 "envy freeness up to any item with high nash welfare the virtue of donating items"):
  with members:
    - "comparing approximate relaxations of envy freeness"
    - "on fair division of indivisible items"
    - "approximation algorithms for computing maximin share allocations"
    - "almost envy freeness with general valuations"
    - "approximating the nash social welfare with budget additive valuations"
    - "finding fair and efficient allocations"
    - "a discrete and bounded envy free cake cutting protocol for any number of agents"
    - "convex program duality fisher markets and nash social welfare"

Connected through hyperedge HE-3 (induced by source paper "optimal budget feasible mechanisms for additive valuations"):
  with members:
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "budget feasible mechanisms on matroids"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"
    - "budget feasible mechanisms for experimental design"
    - "on the approximability of budget feasible mechanisms"
    - "frugal mechanism design via spectral techniques"

Connected through hyperedge HE-4 (induced by source paper "mechanism design with predicted task revenue for bike sharing systems"):
  with members:
    - "a deep reinforcement learning framework for rebalancing dockless bike sharing systems"
    - "online knapsack problem and budgeted truthful bipartite matching"
    - "budget feasible mechanisms"

Connected through hyperedge HE-5 (induced by source paper "on the posted pricing in crowdsourcing power of bonus"):
  with members:
    - "optimal posted prices for online cloud resource allocation"
    - "bayesian budget feasibility with posted pricing"
    - "approval voting and incentives in crowdsourcing"
    - "incentivizing high quality crowdwork"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "reputation based incentive protocols in crowdsourcing applications"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-6 (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"
    - "budget feasible mechanisms for experimental design"
    - "incentives for privacy tradeoff in community sensing"
    - "budget feasible mechanism design from prior free to bayesian"

Connected through hyperedge HE-7 (induced by source paper "simple and efficient budget feasible mechanisms for monotone submodular valuations"):
  with members:
    - "budget feasible mechanism design from prior free to bayesian"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

Connected through hyperedge HE-8 (induced by source paper "budget feasible mechanisms on matroids"):
  with members:
    - "truthful multi unit procurements with budgets"
    - "polyhedral clinching auctions and the adwords polytope"
    - "budget feasible mechanism design from prior free to bayesian"
    - "matroid prophet inequalities"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

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)