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 "optimization with demand oracles"):
  Abstract: We study \emph{combinatorial procurement auctions}, where a buyer with a valuation function $v$ and budget $B$ wishes to buy a set of items. Each item $i$ has a cost $c_i$ and the buyer is interested in a set $S$ that maximizes $v(S)$ subject to $\Sigma_{i\in S}c_i\leq B$. Special cases of combinatorial procurement auctions are classical problems from submodular optimization. In particular, when the costs are all equal (\emph{cardinality constraint}), a classic result by Nemhauser et al shows that the greedy algorithm provides an $\frac e {e-1}$ approximation. #R##N#Motivated by many papers that utilize demand queries to elicit the preferences of agents in economic settings, we develop algorithms that guarantee improved approximation ratios in the presence of demand oracles. We are able to break the $\frac e {e-1}$ barrier: we present algorithms that use only polynomially many demand queries and have approximation ratios of $\frac 9 8+\epsilon$ for the general problem and $\frac 9 8$ for maximization subject to a cardinality constraint. #R##N#We also consider the more general class of subadditive valuations. We present algorithms that obtain an approximation ratio of $2+\epsilon$ for the general problem and 2 for maximization subject to a cardinality constraint. We guarantee these approximation ratios even when the valuations are non-monotone. We show that these ratios are essentially optimal, in the sense that for any constant $\epsilon>0$, obtaining an approximation ratio of $2-\epsilon$ requires exponentially many demand queries.

Includes members:
  Member "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "on the complexity of computing an equilibrium in combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on simultaneous two player combinatorial auctions")
  Member "faster and simpler sketches of valuation functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketching with test scores and submodular maximization")
  Member "budget feasible mechanism design from prior free to bayesian":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "approximations for monotone and non monotone submodular maximization with knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "maximizing non monotone submodular set functions subject to different constraints combined algorithms")
  Member "budget feasible mechanism design via random sampling":
    also connected through other hyperedges:
      (none)
  Member "an impossibility result for truthful combinatorial auctions with submodular valuations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")
  Member "on the approximability of budget feasible mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")

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)