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: "an n to 1 bidder reduction for multi item auctions and its applications"
  Abstract: In this paper, we introduce a novel approach for reducing the $k$-item $n$-bidder auction with additive valuation to $k$-item $1$-bidder auctions. This approach, called the \emph{Best-Guess} reduction, can be applied to address several central questions in optimal revenue auction theory such as the power of randomization, and Bayesian versus dominant-strategy implementations. First, when the items have independent valuation distributions, we present a deterministic mechanism called {\it Deterministic Best-Guess} that yields at least a constant fraction of the optimal revenue by any randomized mechanism. Second, if all the $nk$ valuation random variables are independent, the optimal revenue achievable in {\it dominant strategy incentive compatibility} (DSIC) is shown to be at least a constant fraction of that achievable in {\it Bayesian incentive compatibility} (BIC). Third, when all the $nk$ values are identically distributed according to a common one-dimensional distribution $F$, the optimal revenue is shown to be expressible in the closed form $\Theta(k(r+\int_0^{mr} (1-F(x)^n) \ud x))$ where $r= sup_{x\geq 0} \, x(1 - F(x)^n)$ and $m=\lceil k/n\rceil$; this revenue is achievable by a simple mechanism called \emph{2nd-Price Bundling}. All our results apply to arbitrary distributions, regular or irregular.

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"
    - "tight revenue gaps among simple mechanisms"
    - "a duality based unified approach to bayesian mechanism design"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-2 (induced by source paper "simple credible and approximately optimal auctions"):
  with members:
    - "an improved welfare guarantee for first price auctions"
    - "learning multi item auctions with or without samples"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "sample complexity for non truthful mechanisms"
    - "the price of anarchy in auctions"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

Connected through hyperedge HE-3 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations"):
  with members:
    - "on optimal mechanisms in the two item single buyer unit demand setting"
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "optimal auctions with restricted allocations"
    - "bounding the optimal revenue of selling multiple goods"

Connected through hyperedge HE-4 (induced by source paper "simple mechanisms for profit maximization in multi item auctions"):
  with members:
    - "the best of both worlds asymptotically efficient mechanisms with a guarantee on the expected gains from trade"
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "almost efficient mechanisms for bilateral trading"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "approximately efficient two sided combinatorial auctions"

Connected through hyperedge HE-5 (induced by source paper "on optimal mechanisms in the two item single buyer unit demand setting"):
  with members:
    - "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "optimal auctions with restricted allocations"
    - "selling two goods optimally"
    - "duality and optimality of auctions for uniform distributions"
    - "strong duality for a multiple good monopolist"

Connected through hyperedge HE-6 (induced by source paper "approximation schemes for a unit demand buyer with independent items via symmetries"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "optimal deterministic mechanisms for an additive buyer"
    - "approximate revenue maximization with multiple items"
    - "the menu complexity of one and a half dimensional mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "settling the complexity of computing approximate two player nash equilibria"

Connected through hyperedge HE-7 (induced by source paper "robust revenue maximization under minimal statistical information"):
  with members:
    - "revenue maximization with imprecise distribution"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bayesian auctions with efficient queries"
    - "approximate revenue maximization with multiple items"
    - "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"

Connected through hyperedge HE-8 (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"
    - "a b testing of auctions"
    - "learning simple auctions"

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)