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: "selective call out and real time bidding"
  Abstract: Ads on the Internet are increasingly sold via ad exchanges such as RightMedia, AdECN and Doubleclick Ad Exchange. These exchanges allow real-time bidding, that is, each time the publisher contacts the exchange, the exchange ``calls out'' to solicit bids from ad networks. This aspect of soliciting bids introduces a novel aspect, in contrast to existing literature. This suggests developing a joint optimization framework which optimizes over the allocation and well as solicitation. We model this selective call out as an online recurrent Bayesian decision framework with bandwidth type constraints. We obtain natural algorithms with bounded performance guarantees for several natural optimization criteria. We show that these results hold under different call out constraint models, and different arrival processes. Interestingly, the paper shows that under MHR assumptions, the expected revenue of generalized second price auction with reserve is constant factor of the expected welfare. Also the analysis herein allow us prove adaptivity gap type results for the adwords problem.

Connected through hyperedge HE-1 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "optimal auctions for correlated buyers with sampling"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "revenue optimization in posted price auctions with strategic buyers"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-2 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems"):
  with members:
    - "fast algorithms for online stochastic convex programming"
    - "primal beats dual on online packing lps in the random order model"
    - "online submodular welfare maximization greedy is optimal"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "online stochastic matching beating 1 1 e"
    - "randomized rounding without solving the linear program"

Connected through hyperedge HE-3 (induced by source paper "a general framework for evaluating callout mechanisms in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "beyond equilibria mechanisms for repeated combinatorial auctions"

Connected through hyperedge HE-4 (induced by source paper "budget constrained multi armed bandits with multiple plays"):
  with members:
    - "bandits with knapsacks"
    - "characterizing truthful multi armed bandit mechanisms"

Connected through hyperedge HE-5 (induced by source paper "real time bidding benchmarking with ipinyou dataset"):
  with members:
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization for sponsored search censored learning in mdps"

Connected through hyperedge HE-6 (induced by source paper "real time bidding for online advertising measurement and analysis"):
  with members:
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "online stochastic packing applied to display ad allocation"

Connected through hyperedge HE-7 (induced by source paper "real time bid optimization for group buying ads"):
  with members:
    - "the groupon effect on yelp ratings a root cause analysis"
    - "daily deals prediction social diffusion and reputational ramifications"
    - "bidding for representative allocations for display advertising"

Connected through hyperedge HE-8 (induced by source paper "approximation schemes for sequential posted pricing in multi unit auctions"):
  with members:
    - "mechanism design via correlation gap"
    - "mechanisms for multi unit auctions"
    - "on the power of randomization in algorithmic mechanism design"
    - "budget constrained auctions with heterogeneous items"
    - "dynamic and non uniform pricing strategies for revenue maximization"

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)