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: "a general theory of sample complexity for multi item profit maximization"
  Abstract: The design of profit-maximizing multi-item mechanisms is a notoriously challenging problem with tremendous real-world impact. The mechanism designer's goal is to field a mechanism with high expected profit on the distribution over buyers' values. Unfortunately, if the set of mechanisms he optimizes over is complex, a mechanism may have high empirical profit over a small set of samples but low expected profit. This raises the question, how many samples are sufficient to ensure that the empirically optimal mechanism is nearly optimal in expectation? We uncover structure shared by a myriad of pricing, auction, and lottery mechanisms that allows us to prove strong sample complexity bounds: for any set of buyers' values, profit is a piecewise linear function of the mechanism's parameters. We prove new bounds for mechanism classes not yet studied in the sample-based mechanism design literature and match or improve over the best known guarantees for many classes. The profit functions we study are significantly different from well-understood functions in machine learning, so our analysis requires a sharp understanding of the interplay between mechanism parameters and buyer values. We strengthen our main results with data-dependent bounds when the distribution over buyers' values is "well-behaved." Finally, we investigate a fundamental tradeoff in sample-based mechanism design: complex mechanisms often have higher profit than simple mechanisms, but more samples are required to ensure that empirical and expected profit are close. We provide techniques for optimizing this tradeoff.

Connected through hyperedge HE-1 (induced by source paper "limitations of incentive compatibility on discrete type spaces"):
  with members:
    - "combinatorial auctions via posted prices"

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"
    - "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 "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"
    - "learning multi item auctions with or without samples"

Connected through hyperedge HE-4 (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"
    - "learning multi item auctions with or without samples"
    - "a b testing of auctions"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-5 (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"
    - "learning multi item auctions with or without samples"
    - "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-6 (induced by source paper "semi bandit optimization in the dispersed setting"):
  with members:
    - "data driven clustering via parameterized lloyd s families"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "k center clustering under perturbation resilience"
    - "a pac approach to application specific algorithm selection"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-7 (induced by source paper "adversarial learning for revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "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"
    - "revenue optimization with approximate bid predictions"
    - "a field guide to personalized reserve prices"

Connected through hyperedge HE-8 (induced by source paper "machine learning powered iterative combinatorial auctions"):
  with members:
    - "fast iterative combinatorial auctions via bayesian learning"
    - "a bayesian clearing mechanism for combinatorial auctions"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of revenue maximization"
    - "ironing in the dark"
    - "complexity of mechanism design"

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.LG(Machine Learning)