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: "selling privacy at auction"
  Abstract: We initiate the study of markets for private data, though the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply. On the other hand, the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such auctions can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions for two natural settings which are optimal up to small constant factors: #R##N#1. In the setting in which the data analyst has a fixed accuracy goal, we show that an application of the classic Vickrey auction achieves the analyst's accuracy goal while minimizing his total payment. #R##N#2. In the setting in which the data analyst has a fixed budget, we give a mechanism which maximizes the accuracy of the resulting estimate while guaranteeing that the resulting sum payments do not exceed the analysts budget. #R##N#In both cases, our comparison class is the set of envy-free mechanisms, which correspond to the natural class of fixed-price mechanisms in our setting. #R##N#In both of these results, we ignore the privacy cost due to possible correlations between an individuals private data and his valuation for privacy itself. We then show that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"

Connected through hyperedge HE-2 (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"
    - "truthful mechanisms for agents that value privacy"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "towards data auctions with externalities"):
  with members:
    - "a marketplace for data an algorithmic solution"
    - "low cost learning via active data procurement"
    - "budget feasible mechanisms for experimental design"
    - "conducting truthful surveys cheaply"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism"):
  with members:
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "managing price uncertainty in prosumer centric energy trading a prospect theoretic stackelberg game approach"
    - "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "hardware trojan detection game a prospect theoretic approach"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"

Connected through hyperedge HE-5 (induced by source paper "online pricing with reserve price constraint for personal data markets"):
  with members:
    - "introduction to online convex optimization"
    - "contextual search via intrinsic volumes"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "the design of arbitrage free data pricing schemes"
    - "oracle efficient online learning and auction design"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "multidimensional binary search for contextual decision making"

Connected through hyperedge HE-6 (induced by source paper "prior free data acquisition for accurate statistical estimation"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"
    - "optimal data acquisition for statistical estimation"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "low cost learning via active data procurement"
    - "truthful linear regression"

Connected through hyperedge HE-7 (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"
    - "truthful mechanisms for agents that value privacy"

Connected through hyperedge HE-8 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "quantifying differential privacy under temporal correlations"
    - "how to balance privacy and money through pricing mechanism in personal data market"
    - "private decayed predicate sums on streams"
    - "a theory of pricing private data"

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)