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: "descending price optimally coordinates search"
  Abstract: Investigating potential purchases is often a substantial investment under uncertainty. Standard market designs, such as simultaneous or English auctions, compound this with uncertainty about the price a bidder will have to pay in order to win. As a result they tend to confuse the process of search both by leading to wasteful information acquisition on goods that have already found a good purchaser and by discouraging needed investigations of objects, potentially eliminating all gains from trade. In contrast, we show that the Dutch auction preserves all of its properties from a standard setting without information costs because it guarantees, at the time of information acquisition, a price at which the good can be purchased. Calibrations to start-up acquisition and timber auctions suggest that in practice the social losses through poor search coordination in standard formats are an order of magnitude or two larger than the (negligible) inefficiencies arising from ex-ante bidder asymmetries.

Connected through hyperedge HE-1 (induced by source paper "incentivizing exploration with selective data disclosure"):
  with members:
    - "sample complexity of incentivized exploration"
    - "introduction to multi armed bandits"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"

Connected through hyperedge HE-2 (induced by source paper "sample complexity of incentivized exploration"):
  with members:
    - "introduction to multi armed bandits"
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "bayesian exploration with heterogeneous agents"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "incentivizing exploration with unbiased histories"
    - "competing bandits learning under competition"
    - "fairness incentives for myopic agents"
    - "optimal and myopic information acquisition"
    - "selling to a no regret buyer"

Connected through hyperedge HE-4 (induced by source paper "the markovian price of information"):
  with members:
    - "the price of information in combinatorial optimization"
    - "optimal data acquisition for statistical estimation"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "descending price coordinates approximately efficient search"
    - "online contention resolution schemes"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"
    - "on the adaptivity gap of stochastic orienteering"

Connected through hyperedge HE-5 (induced by source paper "online pandora s boxes and bandits"):
  with members:
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem"
    - "matroid prophet inequalities"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"

Connected through hyperedge HE-6 (induced by source paper "delegated search approximates efficient search"):
  with members:
    - "prophet secretary for combinatorial auctions and matroids"

Connected through hyperedge HE-7 (induced by source paper "beating 1 frac 1 e for ordered prophets"):
  with members:
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "combinatorial auctions via posted prices"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "matroid prophet inequalities"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "adcell ad allocation in cellular networks"
    - "mechanism design via correlation gap"
    - "online stochastic matching beating 1 1 e"

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)