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 "campaign management under approval driven voting rules"):
  Abstract: Approval-like voting rules, such as Sincere-Strategy Preference-Based Approval voting (SP-AV), the Bucklin rule (an adaptive variant of $k$-Approval voting), and the Fallback rule (an adaptive variant of SP-AV) have many desirable properties: for example, they are easy to understand and encourage the candidates to choose electoral platforms that have a broad appeal. In this paper, we investigate both classic and parameterized computational complexity of electoral campaign management under such rules. We focus on two methods that can be used to promote a given candidate: asking voters to move this candidate upwards in their preference order or asking them to change the number of candidates they approve of. We show that finding an optimal campaign management strategy of the first type is easy for both Bucklin and Fallback. In contrast, the second method is computationally hard even if the degree to which we need to affect the votes is small. Nevertheless, we identify a large class of scenarios that admit fixed-parameter tractable algorithms.

Includes members:
  Member "computational aspects of nearly single peaked electorates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "prices matter for the parameterized complexity of shift bribery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal bribery in voting")
  Member "are there any nicely structured preference profiles nearby":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incomplete preferences in single peaked electorates")
  Member "on the computation of fully proportional representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "complexity of manipulation bribery and campaign management in bucklin and fallback voting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal bribery in voting")
  Member "weighted electoral control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "selecting voting locations for fun and profit")
  Member "the complexity of manipulative attacks in nearly single peaked electorates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "determining possible and necessary winners under common voting rules given partial orders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the complexity of possible winners on partial chains")

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)