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: "tight revenue gaps among simple mechanisms"
  Abstract: We consider the simplest and most fundamental problem of selling a single item to a number of buyers whose values are drawn from known independent and regular distributions. There are four most widely-used and widely-studied mechanisms in this literature: Anonymous Posted-Pricing (AP), Second-Price Auction with Anonymous Reserve (AR), Sequential Posted-Pricing (SPM), and Myerson Auction (OPT). Myerson Auction is optimal but complicated, which also suffers a few issues in practice such as fairness; AP is the simplest one but its revenue is also the lowest among these four; AR and SPM are of intermediate complexity and revenue. We study the revenue gaps among these four mechanisms, which is defined as the largest ratio between revenues from two mechanisms. We establish two tight ratios and one tighter bound: #R##N#1. SPM/AP. This ratio studies the power of discrimination in pricing schemes. We obtain the tight ratio of roughly $2.62$, closing the previous known bounds $[e / (e - 1), e]$. #R##N#2. AR/AP. This ratio studies the relative power of auction vs. pricing schemes, when no discrimination is allowed. We get the tight ratio of $\pi^2 / 6 \approx 1.64$, closing the previous known bounds $[e / (e - 1), e]$. #R##N#3. OPT/AR. This ratio studies the power of discrimination in auctions. Previously, the revenue gap is known to be in interval $[2, e]$, and the lower-bound of $2$ is conjectured to be tight~\cite{HR09,H13,AHNPY15}. We disprove this conjecture by obtaining a better lower-bound of $2.15$.

Connected through hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "a neural architecture for designing truthful and efficient auctions"
    - "multi agent adversarial inverse reinforcement learning"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "a duality based unified approach to bayesian mechanism design"
    - "deep sets"
    - "invariant and equivariant graph networks"
    - "tight approximation ratio of anonymous pricing"

Connected through hyperedge HE-2 (induced by source paper "learning reserve prices in second price auctions"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "tight approximation ratio of anonymous pricing"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"

Connected through hyperedge HE-3 (induced by source paper "optimal auctions vs anonymous pricing beyond linear utility"):
  with members:
    - "tight approximation ratio of anonymous pricing"
    - "optimal auctions vs anonymous pricing"
    - "prior independent auctions for risk averse agents"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "the simple economics of approximately optimal auctions"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "sequential posted pricing and multi parameter mechanism design"
    - "mechanism design via correlation gap"

Connected through hyperedge HE-4 (induced by source paper "separation between second price auctions with personalized reserves and the revenue optimal auction"):
  with members:
    - "a field guide to personalized reserve prices"
    - "optimal auctions vs anonymous pricing"

Connected through hyperedge HE-5 (induced by source paper "tight approximation ratio of anonymous pricing"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue gaps for discriminatory and anonymous sequential posted pricing"
    - "revenue gaps for static and dynamic posted pricing of homogeneous goods"
    - "the menu size complexity of revenue approximation"
    - "on the computational complexity of optimal simple mechanisms"

Connected through hyperedge HE-6 (induced by source paper "optimal pricing for mhr and lambda regular distributions"):
  with members:
    - "optimal auctions vs anonymous pricing beyond linear utility"
    - "optimal pricing for mhr distributions"
    - "tight approximation ratio of anonymous pricing"
    - "prophet secretary for combinatorial auctions and matroids"
    - "online market intermediation"
    - "revenue gaps for discriminatory and anonymous sequential posted pricing"
    - "revenue gaps for static and dynamic posted pricing of homogeneous goods"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-7 (induced by source paper "optimal pricing for mhr distributions"):
  with members:
    - "online market intermediation"
    - "revenue gaps for discriminatory and anonymous sequential posted pricing"
    - "randomization beats second price as a prior independent auction"
    - "the vcg mechanism for bayesian scheduling"
    - "optimal auctions vs anonymous pricing"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "extreme value theorems for optimal multidimensional pricing"
    - "mechanism design via correlation gap"

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)