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: "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
  Abstract: We provide a reduction from revenue maximization to welfare maximization in multi-dimensional Bayesian auctions with arbitrary (possibly combinatorial) feasibility constraints and independent bidders with arbitrary (possibly combinatorial) demand constraints, appropriately extending Myerson's result to this setting. We also show that every feasible Bayesian auction can be implemented as a distribution over virtual VCG allocation rules. A virtual VCG allocation rule has the following simple form: Every bidder's type t_i is transformed into a virtual type f_i(t_i), via a bidder-specific function. Then, the allocation maximizing virtual welfare is chosen. Using this characterization, we show how to find and run the revenue-optimal auction given only black box access to an implementation of the VCG allocation rule. We generalize this result to arbitrarily correlated bidders, introducing the notion of a second-order VCG allocation rule. #R##N#We obtain our reduction from revenue to welfare optimization via two algorithmic results on reduced forms in settings with arbitrary feasibility and demand constraints. First, we provide a separation oracle for determining feasibility of a reduced form. Second, we provide a geometric algorithm to decompose any feasible reduced form into a distribution over virtual VCG allocation rules. In addition, we show how to execute both algorithms given only black box access to an implementation of the VCG allocation rule. #R##N#Our results are computationally efficient for all multi-dimensional settings where the bidders are additive. In this case, our mechanisms run in time polynomial in the total number of bidder types, but not type profiles. For generic correlated distributions, this is the natural description complexity of the problem. The runtime can be further improved to poly(#items, #bidders) in item-symmetric settings by making use of recent techniques.

Connected through hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "a neural architecture for designing truthful and efficient auctions"
    - "multi agent adversarial inverse reinforcement learning"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "tight revenue gaps among simple mechanisms"
    - "a duality based unified approach to bayesian mechanism design"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-2 (induced by source paper "complexity theory game theory and economics the barbados lectures"):
  with members:
    - "a round collapse theorem for computationally sound protocols or tfnp is hard on average in pessiland"
    - "on communication complexity of fixed point computation"
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "query to communication lifting for bpp"
    - "lifting randomized query complexity to randomized communication complexity"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "the price of anarchy in auctions"

Connected through hyperedge HE-3 (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"
    - "the menu size complexity of revenue approximation"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "non optimal mechanism design"

Connected through hyperedge HE-4 (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"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "optimal auctions with restricted allocations"
    - "bounding the optimal revenue of selling multiple goods"

Connected through hyperedge HE-5 (induced by source paper "an efficient e bic to bic transformation and its application to black box reduction in revenue maximization"):
  with members:
    - "multi item mechanisms without item independence learnability via robustness"
    - "the complexity of black box mechanism design with priors"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "a duality based unified approach to bayesian mechanism design"
    - "bernoulli factories and black box reductions in mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "revenue maximization and ex post budget constraints"
    - "mechanism design for subadditive agents via an ex ante relaxation"

Connected through hyperedge HE-6 (induced by source paper "on optimal mechanisms in the two item single buyer unit demand setting"):
  with members:
    - "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple mechanisms for subadditive buyers via duality"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "optimal auctions with restricted allocations"
    - "selling two goods optimally"
    - "duality and optimality of auctions for uniform distributions"
    - "strong duality for a multiple good monopolist"

Connected through hyperedge HE-7 (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"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "settling the complexity of computing approximate two player nash equilibria"

Connected through hyperedge HE-8 (induced by source paper "approximately maximizing the broker s profit in a two sided market"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "approximate revenue maximization with multiple items"
    - "muda a truthful multi unit double auction mechanism"
    - "double auctions in markets for multiple kinds of goods"
    - "approximately efficient two sided combinatorial auctions"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "truthful multi unit procurements with budgets"

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)