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: "mechanism design in large games incentives and privacy"
  Abstract: We study the problem of implementing equilibria of complete information games in settings of incomplete information, and address this problem using "recommender mechanisms." A recommender mechanism is one that does not have the power to enforce outcomes or to force participation, rather it only has the power to suggestion outcomes on the basis of voluntary participation. We show that despite these restrictions, recommender mechanisms can implement equilibria of complete information games in settings of incomplete information under the condition that the game is large---i.e. that there are a large number of players, and any player's action affects any other's payoff by at most a small amount. #R##N#Our result follows from a novel application of differential privacy. We show that any algorithm that computes a correlated equilibrium of a complete information game while satisfying a variant of differential privacy---which we call joint differential privacy---can be used as a recommender mechanism while satisfying our desired incentive properties. Our main technical result is an algorithm for computing a correlated equilibrium of a large game while satisfying joint differential privacy. #R##N#Although our recommender mechanisms are designed to satisfy game-theoretic properties, our solution ends up satisfying a strong privacy property as well. No group of players can learn "much" about the type of any player outside the group from the recommendations of the mechanism, even if these players collude in an arbitrary way. As such, our algorithm is able to implement equilibria of complete information games, without revealing information about the realized types.

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"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "asymptotically truthful equilibrium selection in large congestion games"

Connected through hyperedge HE-2 (induced by source paper "scalable and jointly differentially private packing"):
  with members:
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "differentially private contextual linear bandits"
    - "private matchings and allocations"
    - "the possibilities and limitations of private prediction markets"
    - "privacy odometers and filters pay as you go composition"
    - "jointly private convex programming"
    - "truthful linear regression"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-3 (induced by source paper "near optimal jointly private packing algorithms via dual multiplicative weight update"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "inducing approximately optimal flow using truthful mediators"
    - "privately solving linear programs"
    - "fast algorithms for online stochastic convex programming"
    - "buying private data without verification"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "facility location problem in differential privacy model revisited"):
  with members:
    - "scalable and jointly differentially private packing"
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "on facility location with general lower bounds"
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "calibrating data to sensitivity in private data analysis"
    - "approximately optimal mechanism design via differential privacy"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-5 (induced by source paper "optimal truthful and private securities lending"):
  with members:
    - "private matchings and allocations"
    - "censored exploration and the dark pool problem"
    - "selling privacy at auction"

Connected through hyperedge HE-6 (induced by source paper "technical privacy metrics a systematic survey"):
  with members:
    - "evaluating the strength of genomic privacy metrics"
    - "private matchings and allocations"
    - "constructing elastic distinguishability metrics for location privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "from t closeness to differential privacy and vice versa in data anonymization"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "differential privacy an economic method for choosing epsilon"

Connected through hyperedge HE-7 (induced by source paper "learning optimal reserve price against non myopic bidders"):
  with members:
    - "multi scale online learning and its applications to online auctions"
    - "the price of differential privacy for online learning"
    - "learning simple auctions"
    - "jointly private convex programming"
    - "the sample complexity of revenue maximization"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"

Connected through hyperedge HE-8 (induced by source paper "coordinating users of shared facilities via data driven predictive assistants and game theory"):
  with members:
    - "what game are we playing end to end learning in normal and extensive form games"

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)