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: "a b testing of auctions"
  Abstract: For many application areas A/B testing, which partitions users of a system into an A (control) and B (treatment) group to experiment between several application designs, enables Internet companies to optimize their services to the behavioral patterns of their users. Unfortunately, the A/B testing framework cannot be applied in a straightforward manner to applications like auctions where the users (a.k.a., bidders) submit bids before the partitioning into the A and B groups is made. This paper combines auction theoretic modeling with the A/B testing framework to develop methodology for A/B testing auctions. The accuracy of our method %, assuming the auction is directly comparable to ideal A/B testing where there is no interference between A and B. Our results are based on an extension and improved analysis of the inference method of Chawla et al. (2014).

Connected through hyperedge HE-1 (induced by source paper "estimating approximate incentive compatibility"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "optimal auctions through deep learning"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-2 (induced by source paper "online inference for advertising auctions"):
  with members:
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "a tutorial on thompson sampling"
    - "ladder a human level bidding agent for large scale real time online auctions"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "real time bidding for online advertising measurement and analysis"

Connected through hyperedge HE-3 (induced by source paper "inference from auction prices"):
  with members:
    - "econometrics for learning agents"
    - "mechanism design for data science"

Connected through hyperedge HE-4 (induced by source paper "an end to end argument in mechanism design prior independent auctions for budgeted agents"):
  with members:
    - "the price of anarchy in auctions"
    - "randomization beats second price as a prior independent auction"
    - "approximate revenue maximization in interdependent value settings"
    - "mechanism design for data science"
    - "simple and near optimal mechanisms for market intermediation"
    - "price of anarchy for auction revenue"
    - "efficiency guarantees in auctions with budgets"
    - "prior independent mechanisms for scheduling"

Connected through hyperedge HE-5 (induced by source paper "reinforcement mechanism design for e commerce"):
  with members:
    - "reinforcement mechanism design with applications to dynamic pricing in sponsored search auctions"
    - "learning in games robustness of fast convergence"
    - "learning and efficiency in games with dynamic population"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "no regret learning in bayesian games"
    - "memory based control with recurrent neural networks"
    - "learning in auctions regret is hard envy is easy"

Connected through hyperedge HE-6 (induced by source paper "learning and trust in auction markets"):
  with members:
    - "an experimental evaluation of regret based econometrics"
    - "econometrics for learning agents"

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)