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: "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
  Abstract: We address the problem of improving bidders' strategies in prior-dependent revenue-maximizing auctions. We introduce a simple and generic method to design novel bidding strategies if the seller uses past bids to optimize her mechanism. This strategy works with general value distributions, with asymmetric bidders and for different revenue-maximizing mechanisms. Furthermore, it can be made robust to sample approximation errors on the seller part. This results in a large increase in utility for bidders whether they have a full or partial knowledge of their competitors. In the case where the buyer has no information about the competition, we propose a simple and agnostic strategy that is robust to mechanism changes and local (as opposed to global) optimization of e.g. reserve prices by the seller. In textbook-style examples, for instance with uniform value distributions and two bidders, this no-side-information and mechanism-independent strategy yields an enormous 57% increase in buyer utility for lazy second price auctions with no reserves. In the i.i.d symmetric case, we show existence and uniqueness of a Nash equilibrium in the class of strategy we consider for lazy second price auctions, as well as the corresponding explicit shading strategies. Our approach also works for Myerson auctions for instance. At this Nash equilibrium, buyer's utility is the same as in a second price auction with no reserve. Our approach also yields optimal solutions when buyer are constrained in the class of shading strategies they can use, a realistic constraint in practical applications. The heart of our approach is to see optimal auctions in practice as a Stackelberg game where the buyer is the leader, as he is the first one to move (here bid) when the seller is the follower as she has no prior information on the bidder.

Connected through hyperedge HE-1 (induced by source paper "learning to bid in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "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"
    - "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-2 (induced by source paper "robust stackelberg buyers in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "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"
    - "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"

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)