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: "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.

Connected through hyperedge HE-1 (induced by source paper "tight approximation for unconstrained xos maximization"):
  with members:
    - "budget feasible mechanisms"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-2 (induced by source paper "truthful multi unit procurements with budgets"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "clinching auctions beyond hard budget constraints"
    - "optimal provision after wait in healthcare"
    - "online submodular welfare maximization greedy is optimal"
    - "budget feasible mechanism design from prior free to bayesian"
    - "mechanism design without money via stable matching"
    - "on the approximability of budget feasible mechanisms"
    - "frugal mechanism design via spectral techniques"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of computing an equilibrium in combinatorial auctions"):
  with members:
    - "economic efficiency requires interaction"
    - "bayesian sequential auctions"
    - "composable and efficient mechanisms"
    - "simultaneous auctions are almost efficient"
    - "sequential auctions and externalities"
    - "non price equilibria in markets of discrete goods"
    - "price of anarchy for greedy auctions"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-4 (induced by source paper "clearing markets via bundles"):
  with members:
    - "combinatorial walrasian equilibrium"
    - "lower bound for envy free and truthful makespan approximation on related machines"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "envy multi envy and revenue maximization"
    - "bundling equilibrium in combinatorial auctions"

Connected through hyperedge HE-5 (induced by source paper "combinatorial walrasian equilibrium"):
  with members:
    - "clearing markets via bundles"
    - "welfare and revenue guarantees for competitive bundling equilibrium"
    - "combinatorial auctions via posted prices"
    - "understanding incentives mechanism design becomes algorithm design"
    - "lower bound for envy free and truthful makespan approximation on related machines"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "bayesian incentive compatibility via fractional assignments"
    - "envy free makespan approximation"

Connected through hyperedge HE-6 (induced by source paper "budget feasible mechanism design from prior free to bayesian"):
  with members:
    - "bayesian mechanism design for budget constrained agents"
    - "mechanism design without money via stable matching"
    - "selling privacy at auction"
    - "on the approximability of budget feasible mechanisms"
    - "a note on concentration of submodular functions"
    - "the power of randomness in bayesian optimal mechanism design"
    - "bayesian incentive compatibility via fractional assignments"
    - "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)