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: "mechanism design via consensus estimates cross checking and profit extraction"
  Abstract: There is only one technique for prior-free optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distribution on the remaining players. Though quite general, even for digital goods, this random sampling auction has a complicated analysis and is known to be suboptimal. To overcome these issues we generalize the consensus technique from Goldberg and Hartline (2003) to structurally rich environments that include, e.g., single-minded combinatorial auctions.

Connected through hyperedge HE-1 (induced by source paper "ironing in the dark"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "optimal auctions for correlated buyers with sampling"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "mechanism design for data science"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "vickrey auctions for irregular distributions"

Connected through hyperedge HE-2 (induced by source paper "the sample complexity of revenue maximization"):
  with members:
    - "the sample complexity of auctions with side information"
    - "optimal auctions for correlated buyers with sampling"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "mechanism design for data science"
    - "prophet inequalities with limited information"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "sequential posted pricing and multi parameter mechanism design"

Connected through hyperedge HE-3 (induced by source paper "core competitive auctions"):
  with members:
    - "prior free auctions for budgeted agents"
    - "frugal mechanism design via spectral techniques"
    - "frugal and truthful auctions for vertex covers flows and cuts"
    - "frugality ratios and improved truthful mechanisms for vertex cover"

Connected through hyperedge HE-4 (induced by source paper "competitive analysis via benchmark decomposition"):
  with members:
    - "optimal competitive auctions"
    - "on random sampling auctions for digital goods"
    - "the biased sampling profit extraction auction"
    - "prior free auctions for budgeted agents"
    - "envy freedom and prior free mechanism design"
    - "competitive auctions for markets with positive externalities"

Connected through hyperedge HE-5 (induced by source paper "envy freedom and prior free mechanism design"):
  with members:
    - "optimal competitive auctions"
    - "on random sampling auctions for digital goods"
    - "prior free auctions for budgeted agents"
    - "optimal mechansim design and money burning"

Connected through hyperedge HE-6 (induced by source paper "the biased sampling profit extraction auction"):
  with members:
    - "envy freedom and prior free mechanism design"

Connected through hyperedge HE-7 (induced by source paper "prior free auctions for budgeted agents"):
  with members:
    - "polyhedral clinching auctions and the adwords polytope"

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)