human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "competitive analysis via benchmark decomposition"):
  Abstract: We propose a uniform approach for the design and analysis of prior-free competitive auctions and online auctions. Our philosophy is to view the benchmark function as a variable parameter of the model and study a broad class of functions instead of a individual target benchmark. We consider a multitude of well-studied auction settings, and improve upon a few previous results. #R##N#(1) Multi-unit auctions. Given a $\beta$-competitive unlimited supply auction, the best previously known multi-unit auction is $2\beta$-competitive. We design a $(1+\beta)$-competitive auction reducing the ratio from $4.84$ to $3.24$. These results carry over to matroid and position auctions. #R##N#(2) General downward-closed environments. We design a $6.5$-competitive auction improving upon the ratio of $7.5$. Our auction is noticeably simpler than the previous best one. #R##N#(3) Unlimited supply online auctions. Our analysis yields an auction with a competitive ratio of $4.12$, which significantly narrows the margin of $[4,4.84]$ previously known for this problem. #R##N#A particularly important tool in our analysis is a simple decomposition lemma, which allows us to bound the competitive ratio against a sum of benchmark functions. We use this lemma in a "divide and conquer" fashion by dividing the target benchmark into the sum of simpler functions.

Includes members:
  Member "optimal competitive auctions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmark design and prior independent optimization")
  Member "on random sampling auctions for digital goods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "double auctions in markets for multiple kinds of goods")
  Member "the biased sampling profit extraction auction":
    also connected through other hyperedges:
      (none)
  Member "prior free auctions for budgeted agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simple mechanism for a budget constrained buyer")
  Member "envy freedom and prior free mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmark design and prior independent optimization")
  Member "competitive auctions for markets with positive externalities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved efficiency guarantees in auctions with budgets")
  Member "mechanism design via consensus estimates cross checking and profit extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ironing in the dark")

Question: Please tell me which class the source paper 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)