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: "redrawing the boundaries on purchasing data from privacy sensitive individuals"
  Abstract: We prove new positive and negative results concerning the existence of truthful and individually rational mechanisms for purchasing private data from individuals with unbounded and sensitive privacy preferences. We strengthen the impossibility results of Ghosh and Roth (EC 2011) by extending it to a much wider class of privacy valuations. In particular, these include privacy valuations that are based on ({\epsilon}, {\delta})-differentially private mechanisms for non-zero {\delta}, ones where the privacy costs are measured in a per-database manner (rather than taking the worst case), and ones that do not depend on the payments made to players (which might not be observable to an adversary). To bypass this impossibility result, we study a natural special setting where individuals have mono- tonic privacy valuations, which captures common contexts where certain values for private data are expected to lead to higher valuations for privacy (e.g. having a particular disease). We give new mech- anisms that are individually rational for all players with monotonic privacy valuations, truthful for all players whose privacy valuations are not too large, and accurate if there are not too many players with too-large privacy valuations. We also prove matching lower bounds showing that in some respects our mechanism cannot be improved significantly.

Connected through hyperedge HE-1 (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-2 (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"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "truthful mechanisms for agents that value privacy"
    - "selling privacy at auction"

Connected through hyperedge HE-3 (induced by source paper "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"):
  with members:
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "crowdsourcing with tullock contests a new perspective"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "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 "optimal data acquisition for statistical estimation"):
  with members:
    - "strategic classification from revealed preferences"
    - "low cost learning via active data procurement"
    - "truthful linear regression"
    - "buying private data without verification"
    - "conducting truthful surveys cheaply"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "selling privacy at auction"

Connected through hyperedge HE-5 (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"
    - "buying private data without verification"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-6 (induced by source paper "buying private data without verification"):
  with members:
    - "private matchings and allocations"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "conducting truthful surveys cheaply"
    - "mechanism design in large games incentives and privacy"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"

Connected through hyperedge HE-7 (induced by source paper "an anti folk theorem for large repeated games with imperfect monitoring"):
  with members:
    - "the truth behind the myth of the folk theorem"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "mechanism design in large games incentives and privacy"
    - "beating randomized response on incoherent matrices"
    - "truthful mechanisms for agents that value privacy"
    - "privacy aware mechanism design"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-8 (induced by source paper "the empirical implications of privacy aware choice"):
  with members:
    - "truthful mechanisms for agents that value privacy"
    - "privacy aware mechanism design"
    - "selling privacy at auction"

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)