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: "the pseudo dimension of near optimal auctions"
  Abstract: This paper develops a general approach, rooted in statistical learning theory, to learning an approximately revenue-maximizing auction from data. We introduce $t$-level auctions to interpolate between simple auctions, such as welfare maximization with reserve prices, and optimal auctions, thereby balancing the competing demands of expressivity and simplicity. We prove that such auctions have small representation error, in the sense that for every product distribution $F$ over bidders' valuations, there exists a $t$-level auction with small $t$ and expected revenue close to optimal. We show that the set of $t$-level auctions has modest pseudo-dimension (for polynomial $t$) and therefore leads to small learning error. One consequence of our results is that, in arbitrary single-parameter settings, one can learn a mechanism with expected revenue arbitrarily close to optimal from a polynomial number of samples.

Connected through hyperedge HE-1 (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-2 (induced by source paper "online revenue maximization for server pricing"):
  with members:
    - "stability of service under time of use pricing"
    - "era a framework for economic resource allocation for the cloud"
    - "learning simple auctions"
    - "truthful online scheduling with commitments"
    - "the sample complexity of revenue maximization"
    - "combinatorial auctions via posted prices"

Connected through hyperedge HE-3 (induced by source paper "learning multi item auctions with or without samples"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-4 (induced by source paper "selling to a no regret buyer"):
  with members:
    - "learning multi item auctions with or without samples"
    - "on the competition complexity of dynamic mechanism design"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sequential mechanisms with ex post participation guarantees"
    - "optimal dynamic mechanisms with ex post ir via bank accounts"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-5 (induced by source paper "submultiplicative glivenko cantelli and uniform convergence of revenues"):
  with members:
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "making the most of your samples"
    - "relative deviation learning bounds and generalization with unbounded loss functions"

Connected through hyperedge HE-6 (induced by source paper "a sample complexity measure with applications to learning optimal auctions"):
  with members:
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-7 (induced by source paper "simple mechanisms for agents with complements"):
  with members:
    - "simple auctions for agents with complements"
    - "interpolating between truthful and non truthful mechanisms for combinatorial auctions"
    - "greedy algorithms make efficient mechanisms"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "a unifying hierarchy of valuations with complements and substitutes"

Connected through hyperedge HE-8 (induced by source paper "double auctions in markets for multiple kinds of goods"):
  with members:
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "polyhedral clinching auctions for two sided markets"
    - "fixed price approximability of the optimal gain from trade"
    - "approximately efficient two sided combinatorial auctions"
    - "on random sampling auctions for digital goods"
    - "prior independent mechanisms for scheduling"
    - "ascending auctions and walrasian equilibrium"
    - "envy freedom and prior free 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.GT(Computer Science and Game Theory)