human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "prospect theoretic analysis of privacy preserving mechanism"):
  Abstract: We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat to the contributors, the data collector adopts a privacy-preserving mechanism by adding random noise to the computation result, at the cost of reduced accuracy. Individuals decide whether to contribute data when faced with the privacy issue. Due to the intrinsic uncertainty in privacy protection, we model individuals' privacy-related decision using Prospect Theory. Such a theory more accurately models individuals' behavior under uncertainty than the traditional expected utility theory, whose prediction always deviates from practical human behavior. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, the root of which is difficult to compute analytically. We get around this issue by considering a large population approximation, and obtain a closed-form solution that well approximates the precise solution. We discover that the data collector who considers the more realistic Prospect Theory based individual decision modeling would adopt a more conservative privacy-preserving mechanism, compared with the case based on the expected utility theory modeling. We also study the impact of Prospect Theory parameters, and concludes that more loss-averse or risk-seeking individuals will trigger a more conservative mechanism. When individuals have different Prospect Theory parameters, simulations demonstrate that the privacy protection first becomes stronger and then becomes weaker as the heterogeneity increases from a low value to a high one.

Includes members:
  Member "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive honeypot engagement through reinforcement learning of semi markov decision processes")
  Member "managing price uncertainty in prosumer centric energy trading a prospect theoretic stackelberg game approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "peer to peer energy trading with sustainable user participation a game theoretic approach")
  Member "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing":
    also connected through other hyperedges:
      (none)
  Member "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uav communications for 5g and beyond recent advances and future trends")
  Member "hardware trojan detection game a prospect theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a game of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations")
  Member "prospect theory for enhanced smart grid resilience using distributed energy storage":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a game of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations")
  Member "market model and optimal pricing scheme of big data and internet of things iot":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a crowdsourcing framework for on device federated learning")
  Member "the value of privacy strategic data subjects incentive mechanisms and fundamental limits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "impact of social learning on privacy preserving data collection")

Question: Please tell me which class the source paper 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)