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: "optimal and benchmark optimal competition complexity for additive buyers over independent items"
  Abstract: The Competition Complexity of an auction setting refers to the number of additional bidders necessary in order for the (deterministic, prior-independent, dominant strategy truthful) Vickrey-Clarke-Groves mechanism to achieve greater revenue than the (randomized, prior-dependent, Bayesian-truthful) optimal mechanism without the additional bidders. #R##N#We prove that the competition complexity of $n$ bidders with additive valuations over $m$ independent items is at most $n(\ln(1+m/n)+2)$, and also at most $9\sqrt{nm}$. When $n \leq m$, the first bound is optimal up to constant factors, even when the items are i.i.d. and regular. When $n \geq m$, the second bound is optimal for the benchmark introduced in [EFFTW17a] up to constant factors, even when the items are i.i.d. and regular. We further show that, while the Eden et al. benchmark is not necessarily tight in the $n \geq m$ regime, the competition complexity of $n$ bidders with additive valuations over even $2$ i.i.d. regular items is indeed $\omega(1)$. #R##N#Our main technical contribution is a reduction from analyzing the Eden et al. benchmark to proving stochastic dominance of certain random variables.

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

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)