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: "revenue maximization for selling multiple correlated items"
  Abstract: We study the problem of selling $n$ items to a single buyer with an additive valuation function. We consider the valuation of the items to be correlated, i.e., desirabilities of the buyer for the items are not drawn independently. Ideally, the goal is to design a mechanism to maximize the revenue. However, it has been shown that a revenue optimal mechanism might be very complicated and as a result inapplicable to real-world auctions. Therefore, our focus is on designing a simple mechanism that achieves a constant fraction of the optimal revenue. Babaioff et al. propose a simple mechanism that achieves a constant fraction of the optimal revenue for independent setting with a single additive buyer. However, they leave the following problem as an open question: "Is there a simple, approximately optimal mechanism for a single additive buyer whose value for $n$ items is sampled from a common base-value distribution?" #R##N#Babaioff et al. show a constant approximation factor of the optimal revenue can be achieved by either selling the items separately or as a whole bundle in the independent setting. We show a similar result for the correlated setting when the desirabilities of the buyer are drawn from a common base-value distribution. It is worth mentioning that the core decomposition lemma which is mainly the heart of the proofs for efficiency of the mechanisms does not hold for correlated settings. Therefore we propose a modified version of this lemma which is applicable to the correlated settings as well. Although we apply this technique to show the proposed mechanism can guarantee a constant fraction of the optimal revenue in a very weak correlation, this method alone can not directly show the efficiency of the mechanism in stronger correlations.

Connected through hyperedge HE-1 (induced by source paper "prophet inequalities with linear correlations and augmentations"):
  with members:
    - "optimal single choice prophet inequalities from samples"
    - "robust algorithms for the secretary problem"
    - "prophet secretary through blind strategies"
    - "stochastic bandits robust to adversarial corruptions"
    - "optimal online contention resolution schemes via ex ante prophet inequalities"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "prophet secretary for combinatorial auctions and matroids"

Connected through hyperedge HE-2 (induced by source paper "buy many mechanisms are not much better than item pricing"):
  with members:
    - "optimal deterministic mechanisms for an additive buyer"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "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"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "sampling and representation complexity of revenue maximization"

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"
    - "the value of information concealment"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a simple mechanism for a budget constrained buyer"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "revenue maximization and ex post budget constraints"
    - "the menu size complexity of revenue approximation"
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "mixture selection mechanism design and signaling"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-5 (induced by source paper "99 revenue via enhanced competition"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "on the competition complexity of dynamic mechanism design"
    - "on revenue monotonicity in combinatorial auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "reaping the benefits of bundling under high production costs"
    - "mechanism design for subadditive agents via an ex ante relaxation"

Connected through hyperedge HE-6 (induced by source paper "optimal deterministic mechanisms for an additive buyer"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "on the complexity of bundle pricing and simple mechanisms"
    - "on revenue monotonicity in combinatorial auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "strong duality for a multiple good monopolist"

Connected through hyperedge HE-7 (induced by source paper "third party data providers ruin simple mechanisms"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "on the computational complexity of optimal simple mechanisms"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "mixture selection mechanism design and signaling"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-8 (induced by source paper "pricing multi unit markets"):
  with members:
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "the invisible hand of dynamic market pricing"
    - "pricing online decisions beyond auctions"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "combinatorial auctions via posted prices"
    - "simultaneous auctions are almost efficient"
    - "dynamic pricing with limited supply"
    - "non price equilibria in markets of discrete goods"

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)