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 "segmentation incentives and privacy"):
  Abstract: Data-driven segmentation is the powerhouse behind the success of online advertising. Various underlying challenges for successful segmentation have been studied by the academic community, with one notable exception—consumers’ incentives have been typically ignored. This lacuna is troubling, as consumers have much control over the data being collected. Missing or manipulated data could lead to inferior segmentation. The current work proposes a model of prior-free segmentation, inspired by models of facility location and, to the best of our knowledge, provides the first segmentation mechanism that addresses incentive compatibility, efficient market segmentation, and privacy in the absence of a common prior.

Includes members:
  Member "the strange case of privacy in equilibrium models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "belief invariant equilibria in games with incomplete information")
  Member "privacy and mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locally private hypothesis selection")
  Member "the exponential mechanism for social welfare private truthful and nearly optimal":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a differentially private incentive design for traffic offload to public transportation")
  Member "privacy aware mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "price of privacy in the keynesian beauty contest")
  Member "approximately optimal mechanism design via differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "facility location problem in differential privacy model revisited")

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)