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: "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.

Connected through hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact"):
  with members:
    - "differentially private double spectrum auction with approximate social welfare maximization"
    - "strategic classification from revealed preferences"
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "inducing approximately optimal flow using truthful mediators"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-2 (induced by source paper "jointly private convex programming"):
  with members:
    - "private matchings and allocations"
    - "privately solving linear programs"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "differential privacy for the analyst via private equilibrium computation"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"

Connected through hyperedge HE-3 (induced by source paper "coordination complexity small information coordinating large populations"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "learning and efficiency in games with dynamic population"
    - "welfare maximization with limited interaction"
    - "inducing approximately optimal flow using truthful mediators"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "economic efficiency requires interaction"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "inducing approximately optimal flow using truthful mediators"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "achieving target equilibria in network routing games without knowing the latency functions"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-5 (induced by source paper "robust mediators in large games"):
  with members:
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "differential privacy for the analyst via private equilibrium computation"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-6 (induced by source paper "higher order approximate relational refinement types for mechanism design and differential privacy"):
  with members:
    - "dual query practical private query release for high dimensional data"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "on the power of randomization in algorithmic mechanism design"

Connected through hyperedge HE-7 (induced by source paper "finding any nontrivial coarse correlated equilibrium is hard"):
  with members:
    - "inapproximability of nash equilibrium"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "settling the complexity of computing two player nash equilibria"

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.DS(Data Structures and Algorithms)