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: "learning multi item auctions with or without samples"
  Abstract: We provide algorithms that learn simple auctions whose revenue is approximately optimal in multi-item multi-bidder settings, for a wide range of valuations including unit-demand, additive, constrained additive, XOS, and subadditive. We obtain our learning results in two settings. The first is the commonly studied setting where sample access to the bidders' distributions over valuations is given, for both regular distributions and arbitrary distributions with bounded support. Our algorithms require polynomially many samples in the number of items and bidders. The second is a more general max-min learning setting that we introduce, where we are given "approximate distributions," and we seek to compute an auction whose revenue is approximately optimal simultaneously for all "true distributions" that are close to the given ones. These results are more general in that they imply the sample-based results, and are also applicable in settings where we have no sample access to the underlying distributions but have estimated them indirectly via market research or by observation of previously run, potentially non-truthful auctions. #R##N#Our results hold for valuation distributions satisfying the standard (and necessary) independence-across-items property. They also generalize and improve upon recent works, which have provided algorithms that learn approximately optimal auctions in more restricted settings with additive, subadditive and unit-demand valuations using sample access to distributions. We generalize these results to the complete unit-demand, additive, and XOS setting, to i.i.d. subadditive bidders, and to the max-min setting. #R##N#Our results are enabled by new uniform convergence bounds for hypotheses classes under product measures. Our bounds result in exponential savings in sample complexity compared to bounds derived by bounding the VC dimension, and are of independent interest.

Connected through hyperedge HE-1 (induced by source paper "simple credible and approximately optimal auctions"):
  with members:
    - "an improved welfare guarantee for first price auctions"
    - "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"
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"

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 revenue gaps among simple mechanisms"
    - "tight approximation ratio of anonymous pricing"
    - "a general theory of sample complexity for multi item profit maximization"
    - "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 "bulow klemperer style results for welfare maximization in two sided markets"):
  with members:
    - "the vickrey auction with a single duplicate bidder approximates the optimal revenue"
    - "99 revenue via enhanced competition"
    - "the best of both worlds asymptotically efficient mechanisms with a guarantee on the expected gains from trade"
    - "are two samples really better than one on the non asymptotic performance of empirical revenue maximization"
    - "optimal and benchmark optimal competition complexity for additive buyers over independent items"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "submultiplicative glivenko cantelli and uniform convergence of revenues"
    - "a general theory of sample complexity for multi item profit maximization"

Connected through hyperedge HE-4 (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"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"

Connected through hyperedge HE-5 (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"
    - "a b testing of auctions"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-6 (induced by source paper "settling the sample complexity of single parameter revenue maximization"):
  with members:
    - "the sample complexity of up to varepsilon multi dimensional revenue maximization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "on revenue monotonicity in combinatorial auctions"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-7 (induced by source paper "algorithmic price discrimination"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "learning simple auctions"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity for non truthful mechanisms"
    - "the sample complexity of revenue maximization"
    - "bayesian incentive compatible bandit exploration"

Connected through hyperedge HE-8 (induced by source paper "generalizing complex hypotheses on product distributions auctions prophet inequalities and pandora s problem"):
  with members:
    - "a general theory of sample complexity for multi item profit maximization"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "mechanism design via optimal transport"

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)