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 menu size complexity of revenue approximation"
  Abstract: We consider a monopolist that is selling $n$ items to a single additive buyer, where the buyer's values for the items are drawn according to independent distributions $F_1, F_2,\ldots,F_n$ that possibly have unbounded support. It is well known that - unlike in the single item case - the revenue-optimal auction (a pricing scheme) may be complex, sometimes requiring a continuum of menu entries. It is also known that simple auctions with a finite bounded number of menu entries can extract a constant fraction of the optimal revenue. Nonetheless, the question of the possibility of extracting an arbitrarily high fraction of the optimal revenue via a finite menu size remained open. #R##N#In this paper, we give an affirmative answer to this open question, showing that for every $n$ and for every $\varepsilon>0$, there exists a complexity bound $C=C(n,\varepsilon)$ such that auctions of menu size at most $C$ suffice for obtaining a $(1-\varepsilon)$ fraction of the optimal revenue from any $F_1,\ldots,F_n$. We prove upper and lower bounds on the revenue approximation complexity $C(n,\varepsilon)$, as well as on the deterministic communication complexity required to run an auction that achieves such an approximation.

Connected through hyperedge HE-1 (induced by source paper "the complexity of contracts"):
  with members:
    - "delegated search approximates efficient search"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bernoulli factories and black box reductions in mechanism design"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "non optimal mechanism design"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-2 (induced by source paper "optimal mechanism design for single minded agents"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "optimal multi unit mechanisms with private demands"
    - "the menu complexity of one and a half dimensional mechanism design"
    - "optimal mechanisms with simple menus"
    - "mechanism design via optimal transport"
    - "an algorithmic characterization of multi dimensional mechanisms"

Connected through hyperedge HE-3 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations"):
  with members:
    - "on optimal mechanisms in the two item single buyer unit demand setting"
    - "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"
    - "optimal auctions with restricted allocations"
    - "bounding the optimal revenue of selling multiple goods"
    - "selling two goods optimally"

Connected through hyperedge HE-4 (induced by source paper "approximation schemes for a unit demand buyer with independent items via symmetries"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "optimal deterministic mechanisms for an additive buyer"
    - "approximate revenue maximization with multiple items"
    - "the menu complexity of one and a half dimensional mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

Connected through hyperedge HE-5 (induced by source paper "estimating approximate incentive compatibility"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "optimal auctions through deep learning"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a b testing of auctions"
    - "learning simple auctions"

Connected through hyperedge HE-6 (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"
    - "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"
    - "revenue maximization for selling multiple correlated items"

Connected through hyperedge HE-7 (induced by source paper "approximation schemes for a buyer with independent items via symmetries"):
  with members:
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "strong duality for a multiple good monopolist"
    - "understanding incentives mechanism design becomes algorithm design"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "an algorithmic characterization of multi dimensional mechanisms"
    - "the power of randomness in bayesian optimal mechanism design"

Connected through hyperedge HE-8 (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"
    - "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"
    - "sampling and representation complexity of revenue maximization"

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)