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: "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
  Abstract: We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. #R##N#In this paper, the value of $\epsilon$ units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of $\epsilon$. The higher $\epsilon$ is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy $\epsilon$ units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum.

Connected through hyperedge HE-1 (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"
    - "spectrum investment under uncertainty a behavioral economics perspective"

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"
    - "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"
    - "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:
    - "crowdsourcing with tullock contests a new perspective"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "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 "preserving privacy enables coexistence equilibrium of competitive diffusion in social networks"):
  with members:
    - "make up your mind the price of online queries in differential privacy"
    - "competitive diffusion in social networks quality or seeding"
    - "prince privacy preserving mechanisms for influence diffusion in online social networks"
    - "virality prediction and community structure in social networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "diffusion in social networks with competing products"
    - "competitive contagion in networks"

Connected through hyperedge HE-5 (induced by source paper "theseus incentivizing truth discovery in mobile crowd sensing systems"):
  with members:
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "providing long term participation incentive in participatory sensing"
    - "crowdsourcing with tullock contests a new perspective"
    - "truthful linear regression"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "linear regression as a non cooperative game"

Connected through hyperedge HE-6 (induced by source paper "de anonymization of social networks with communities when quantifications meet algorithms"):
  with members:
    - "optimal de anonymization in random graphs with community structure"
    - "impact of clustering on the performance of network de anonymization"
    - "de anonymizing social networks"

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)