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: "linear regression as a non cooperative game"
  Abstract: Linear regression amounts to estimating a linear model that maps features (e.g., age or gender) to corresponding data (e.g., the answer to a survey or the outcome of a medical exam). It is a ubiquitous tool in experimental sciences. We study a setting in which features are public but the data is private information. While the estimation of the linear model may be useful to participating individuals, (if, e.g., it leads to the discovery of a treatment to a disease), individuals may be reluctant to disclose their data due to privacy concerns. In this paper, we propose a generic game-theoretic model to express this trade-off. Users add noise to their data before releasing it. In particular, they choose the variance of this noise to minimize a cost comprising two components: (a) a privacy cost, representing the loss of privacy incurred by the release; and (b) an estimation cost, representing the inaccuracy in the linear model estimate. We study the Nash equilibria of this game, establishing the existence of a unique non-trivial equilibrium. We determine its efficiency for several classes of privacy and estimation costs, using the concept of the price of stability. Finally, we prove that, for a specific estimation cost, the generalized least-square estimator is optimal among all linear unbiased estimators in our non-cooperative setting: this result extends the famous Aitken/Gauss-Markov theorem in statistics, establishing that its conclusion persists even in the presence of strategic individuals.

Connected through hyperedge HE-1 (induced by source paper "causal feature discovery through strategic modification"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "truthful linear regression"
    - "budget feasible mechanisms for experimental design"
    - "buying private data without verification"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fair multi party machine learning a game theoretic approach"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "deep residual learning for image recognition"
    - "a game theoretic study on non monetary incentives in data analytics projects with privacy implications"
    - "linear regression from strategic data sources"

Connected through hyperedge HE-4 (induced by source paper "strategyproof linear regression in high dimensions"):
  with members:
    - "strategic classification from revealed preferences"
    - "learning from untrusted data"
    - "truthful linear regression"
    - "optimal impartial selection"

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"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "truthful linear regression"):
  with members:
    - "private matchings and allocations"
    - "budget feasible mechanisms for experimental design"
    - "buying private data without verification"
    - "calibrating data to sensitivity in private data analysis"
    - "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"
    - "truthful mechanisms for agents that value privacy"

Connected through hyperedge HE-7 (induced by source paper "a game theoretic study on non monetary incentives in data analytics projects with privacy implications"):
  with members:
    - "budget feasible mechanisms for experimental design"
    - "what s the gist privacy preserving aggregation of user profiles"
    - "crowd assisted search for price discrimination in e commerce first results"
    - "conducting truthful surveys cheaply"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "truthful mechanisms for agents that value privacy"
    - "selling privacy at auction"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-8 (induced by source paper "linear regression from strategic data sources"):
  with members:
    - "elicitation for aggregation"
    - "budget feasible mechanisms for experimental design"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "privacy auctions for recommender systems"
    - "selling privacy at auction"
    - "approximately optimal mechanism design via differential 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)