human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "privacy and truthful equilibrium selection for aggregative games"):
  Abstract: We study a very general class of games --- multi-dimensional aggregative games --- which in particular generalize both anonymous games and weighted congestion games. For any such game that is also large, we solve the equilibrium selection problem in a strong sense. In particular, we give an efficient weak mediator: a mechanism which has only the power to listen to reported types and provide non-binding suggested actions, such that (a) it is an asymptotic Nash equilibrium for every player to truthfully report their type to the mediator, and then follow its suggested action; and (b) that when players do so, they end up coordinating on a particular asymptotic pure strategy Nash equilibrium of the induced complete information game. In fact, truthful reporting is an ex-post Nash equilibrium of the mediated game, so our solution applies even in settings of incomplete information, and even when player types are arbitrary or worst-case (i.e. not drawn from a common prior). We achieve this by giving an efficient differentially private algorithm for computing a Nash equilibrium in such games. The rates of convergence to equilibrium in all of our results are inverse polynomial in the number of players $n$. We also apply our main results to a multi-dimensional market game. #R##N#Our results can be viewed as giving, for a rich class of games, a more robust version of the Revelation Principle, in that we work with weaker informational assumptions (no common prior), yet provide a stronger solution concept (ex-post Nash versus Bayes Nash equilibrium). In comparison to previous work, our main conceptual contribution is showing that weak mediators are a game theoretic object that exist in a wide variety of games -- previously, they were only known to exist in traffic routing games.

Includes members:
  Member "private matchings and allocations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact")
  Member "finding any nontrivial coarse correlated equilibrium is hard":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "no regret learning in cournot games")
  Member "privately solving linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient noise tolerant and private learning via boosting")
  Member "on the complexity of nash equilibria in anonymous games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ppp completeness with connections to cryptography")
  Member "privacy preserving public information for sequential games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamically protecting privacy under uncertainty")
  Member "approximately stable school optimal and student truthful many to one matchings via differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentially private call auctions and market impact")
  Member "asymptotically truthful equilibrium selection in large congestion games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentially private call auctions and market impact")
  Member "privacy and mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "segmentation incentives and privacy")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)