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: "ironing in the dark"
  Abstract: This paper presents the first polynomial-time algorithm for position and matroid auction environments that learns, from samples from an unknown bounded valuation distribution, an auction with expected revenue arbitrarily close to the maximum possible. In contrast to most previous work, our results apply to arbitrary (not necessarily regular) distributions and the strongest possible benchmark, the Myerson-optimal auction. Learning a near-optimal auction for an irregular distribution is technically challenging because it requires learning the appropriate "ironed intervals," a delicate global property of the distribution.

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 "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"
    - "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-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 "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"
    - "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"

Connected through hyperedge HE-5 (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 general theory of sample complexity for multi item profit maximization"
    - "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"
    - "complexity of mechanism design"

Connected through hyperedge HE-6 (induced by source paper "incentive aware contextual pricing with non parametric market noise"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "lp based approximation for personalized reserve prices"
    - "semi parametric dynamic contextual pricing"
    - "nonparametric learning and optimization with covariates"
    - "learning optimal reserve price against non myopic bidders"
    - "contextual search via intrinsic volumes"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"

Connected through hyperedge HE-7 (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"
    - "learning multi item auctions with or without samples"
    - "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"
    - "mechanism design via optimal transport"

Connected through hyperedge HE-8 (induced by source paper "the ad types problem"):
  with members:
    - "sponsored search auctions with rich ads"
    - "note on vcg vs price raising for matching markets"
    - "fast core pricing for rich advertising auctions"
    - "ad auctions and cascade model gsp inefficiency and algorithms"
    - "sponsored search market equilibria and the hungarian method"
    - "sponsored search auctions with markovian users"

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)