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: "truthful mechanisms for agents that value privacy"
  Abstract: Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from the truthfulness; it is not incorporated in players' utility functions (and doing so has been shown to lead to non-truthfulness in some cases). In this work, we propose a new, general way of modelling privacy in players' utility functions. Specifically, we only assume that if an outcome $o$ has the property that any report of player $i$ would have led to $o$ with approximately the same probability, then $o$ has small privacy cost to player $i$. We give three mechanisms that are truthful with respect to our modelling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number $n$ of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of $n$).

Connected through hyperedge HE-1 (induced by source paper "differential privacy at risk bridging randomness and privacy budget"):
  with members:
    - "federated learning with bayesian differential privacy"
    - "issues encountered deploying differential privacy"
    - "pain free random differential privacy with sensitivity sampling"
    - "concentrated differential privacy"
    - "differential privacy an economic method for choosing epsilon"
    - "functional mechanism regression analysis under differential privacy"
    - "selling privacy at auction"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "price of privacy in the keynesian beauty contest"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "linear queries estimation with local differential privacy"
    - "locally private gaussian estimation"
    - "perception games and privacy"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy aware mechanism design"
    - "what can we learn privately"

Connected through hyperedge HE-3 (induced by source paper "impact of social learning on privacy preserving data collection"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "selling privacy at auction"

Connected through hyperedge HE-4 (induced by source paper "individual sensitivity preprocessing for data privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "tight lower bounds for locally differentially private selection"
    - "the possibilities and limitations of private prediction markets"
    - "adaptive learning with robust generalization guarantees"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "heterogeneous differential privacy"
    - "truthful linear regression"
    - "buying private data without verification"

Connected through hyperedge HE-5 (induced by source paper "millionaire a hint guided approach for crowdsourcing"):
  with members:
    - "end to end learning for self driving cars"
    - "crowdsourcing with unsure option"
    - "on the convergence of a family of robust losses for stochastic gradient descent"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "incentivizing high quality crowdwork"
    - "training convolutional networks with noisy labels"
    - "imagenet large scale visual recognition challenge"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "perception games and privacy"):
  with members:
    - "the empirical implications of privacy aware choice"
    - "privacy aware mechanism design"

Connected through hyperedge HE-7 (induced by source paper "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"):
  with members:
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "privacy games optimal user centric data obfuscation"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "buying private data without verification"
    - "on the relation between identifiability differential privacy and mutual information privacy"

Connected through hyperedge HE-8 (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"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "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"

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)