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: "on optimal multi dimensional mechanism design"
  Abstract: We efficiently solve the optimal multi-dimensional mechanism design problem for independent bidders with arbitrary demand constraints when either the number of bidders is a constant or the number of items is a constant. In the first setting, we need that each bidder's values for the items are sampled from a possibly correlated, item-symmetric distribution, allowing different distributions for each bidder. In the second setting, we allow the values of each bidder for the items to be arbitrarily correlated, but assume that the distribution of bidder types is bidder-symmetric. #R##N#For all eps>0, we obtain an additive eps-approximation, when the value distributions are bounded, or a multiplicative (1-eps)-approximation when the value distributions are unbounded, but satisfy the Monotone Hazard Rate condition, covering a widely studied class of distributions in Economics. Our runtime is polynomial in max{#items,#bidders}, and not the size of the support of the joint distribution of all bidders' values for all items, which is typically exponential in both the number of items and the number of bidders. Our mechanisms are randomized, explicitly price bundles, and can sometimes accommodate budget constraints. #R##N#Our results are enabled by establishing several new tools and structural properties of Bayesian mechanisms. We provide a symmetrization technique turning any truthful mechanism into one that has the same revenue and respects all symmetries in the underlying value distributions. We also prove that item-symmetric mechanisms satisfy a natural monotonicity property which, unlike cyclic-monotonicity, can be harnessed algorithmically. Finally, we provide a technique that turns any given eps-BIC mechanism (i.e. one where incentive constraints are violated by eps) into a truly-BIC mechanism at the cost of O(sqrt{eps}) revenue. We expect our tools to be used beyond the settings we consider here.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "from bayesian to crowdsourced bayesian auctions"):
  with members:
    - "bayesian auctions with efficient queries"
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-4 (induced by source paper "approximate revenue maximization with multiple items"):
  with members:
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"
    - "a note on the ratio of revenues between selling in a bundle and separately"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "duality and optimality of auctions for uniform distributions"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "sampling and representation complexity of revenue maximization"
    - "strong duality for a multiple good monopolist"

Connected through hyperedge HE-5 (induced by source paper "reducing revenue to welfare maximization approximation algorithms and other generalizations"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "matroid prophet inequalities"
    - "simple and nearly optimal multi item auctions"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "an algorithmic characterization of multi dimensional mechanisms"
    - "extreme value theorems for optimal multidimensional pricing"

Connected through hyperedge HE-6 (induced by source paper "the menu size complexity of auctions"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "duality and optimality of auctions for uniform distributions"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "mechanism design via optimal transport"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "the complexity of optimal mechanism design"
    - "an algorithmic characterization of multi dimensional mechanisms"

Connected through hyperedge HE-7 (induced by source paper "optimal mechanisms with simple menus"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "mechanism design via optimal transport"
    - "the menu size complexity of auctions"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "simple and nearly optimal multi item auctions"
    - "an algorithmic characterization of multi dimensional mechanisms"

Connected through hyperedge HE-8 (induced by source paper "extreme value theorems for optimal multidimensional pricing"):
  with members:
    - "bayesian truthful mechanisms for job scheduling from bi criterion approximation algorithms"
    - "the complexity of optimal multidimensional pricing"
    - "understanding incentives mechanism design becomes algorithm design"
    - "mechanism design via optimal transport"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "the complexity of optimal mechanism design"
    - "simple and nearly optimal multi item auctions"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"

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)