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: "dynamic reserve prices for repeated auctions learning from bids"
  Abstract: A large fraction of online advertisement is sold via repeated second price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: Can changing the reserve prices based on the previous bids improve the revenue of the auction, taking into account the long-term incentives and strategic behavior of the bidders? We show that if the distribution of the valuations is known and satisfies the standard regularity assumptions, then the optimal mechanism has a constant reserve. However, when there is uncertainty in the distribution of the valuations, previous bids can be used to learn the distribution of the valuations and to update the reserve price. We present a simple, approximately incentive-compatible, and asymptotically optimal dynamic reserve mechanism that can significantly improve the revenue over the best static reserve. #R##N#The paper is from July 2014 (our submission to WINE 2014), posted later here on the arxiv to complement the 1-page abstract in the WINE 2014 proceedings.

Connected through hyperedge HE-1 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions"):
  with members:
    - "contextual search via intrinsic volumes"
    - "multi armed bandit problems with strategic arms"
    - "perishability of data dynamic pricing under varying coefficient models"
    - "a field guide to personalized reserve prices"
    - "multidimensional binary search for contextual decision making"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "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 "protecting consumers against personalized pricing a stopping time approach"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "semi parametric dynamic contextual pricing"
    - "learning prices for repeated auctions with strategic buyers"

Connected through hyperedge HE-3 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders"):
  with members:
    - "optimal pricing in repeated posted price auctions"
    - "learning optimal reserve price against non myopic bidders"
    - "contextual search via intrinsic volumes"
    - "robust repeated auctions under heterogeneous buyer behavior"
    - "optimal dynamic auctions are virtual welfare maximizers"
    - "dynamic pricing with finitely many unknown valuations"
    - "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"
    - "revenue optimization with approximate bid predictions"

Connected through hyperedge HE-4 (induced by source paper "adversarial learning for revenue maximizing auctions"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "learning to bid in revenue maximizing auctions"
    - "lp based approximation for personalized reserve prices"
    - "learning to clear the market"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a general theory of sample complexity for multi item profit maximization"
    - "revenue optimization with approximate bid predictions"
    - "a field guide to personalized reserve prices"

Connected through hyperedge HE-5 (induced by source paper "learning to bid in revenue maximizing auctions"):
  with members:
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "learning to bid without knowing your value"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-6 (induced by source paper "robust stackelberg buyers in repeated auctions"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of revenue maximization"
    - "making the most of your samples"

Connected through hyperedge HE-7 (induced by source paper "contextual bandits with cross learning"):
  with members:
    - "incentive aware contextual pricing with non parametric market noise"
    - "learning to bid without knowing your value"
    - "learning multi item auctions with or without samples"
    - "selling to a no regret buyer"
    - "learning simple auctions"
    - "no regret learning in bayesian games"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-8 (induced by source paper "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"):
  with members:
    - "learning to bid in revenue maximizing auctions"
    - "online learning for measuring incentive compatibility in ad auctions"
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of 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)