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 value of information concealment"
  Abstract: We consider a revenue optimizing seller selling a single item to a buyer, on whose private value the seller has a noisy signal. We show that, when the signal is kept private, arbitrarily more revenue could potentially be extracted than if the signal is leaked or revealed. We then show that, if the seller is not allowed to make payments to the buyer, the gap between the two is bounded by a multiplicative factor of 3, if the value distribution conditioning on each signal is regular. We give examples showing that both conditions are necessary for a constant bound to hold. #R##N#We connect this scenario to multi-bidder single-item auctions where bidders' values are correlated. Similarly to the setting above, we show that the revenue of a Bayesian incentive compatible, ex post individually rational auction can be arbitrarily larger than that of a dominant strategy incentive compatible auction, whereas the two are no more than a factor of 5 apart if the auctioneer never pays the bidders and if each bidder's value conditioning on the others' is drawn according to a regular distribution. The upper bounds in both settings degrade gracefully when the distribution is a mixture of a small number of regular distributions.

Connected through hyperedge HE-1 (induced by source paper "persuasion and incentives through the lens of duality"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "on the competition complexity of dynamic mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "algorithmic persuasion with no externalities"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "computational aspects of private bayesian persuasion"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-2 (induced by source paper "simple and approximately optimal pricing for proportional complementarities"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "on the competition complexity of dynamic mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "reaping the benefits of bundling under high production costs"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-3 (induced by source paper "a duality based unified approach to bayesian mechanism design"):
  with members:
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "selling two goods optimally"

Connected through hyperedge HE-4 (induced by source paper "smoothed analysis of multi item auctions with correlated values"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "smoothed and average case approximation ratios of mechanisms beyond the worst case analysis"
    - "simple mechanisms for subadditive buyers via duality"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "optimal competitive auctions"
    - "an n to 1 bidder reduction for multi item auctions and its applications"

Connected through hyperedge HE-5 (induced by source paper "optimal and benchmark optimal competition complexity for additive buyers over independent items"):
  with members:
    - "99 revenue via enhanced competition"
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

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)