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 "computational aspects of nearly single peaked electorates"):
  Abstract: Manipulation, bribery, and control are well-studied ways of changing the outcome of an election. Many voting rules are, in the general case, computationally resistant to some of these manipulative actions. However when restricted to single-peaked electorates, these rules suddenly become easy to manipulate. Recently, Faliszewski, Hemaspaandra, and Hemaspaandra studied the computational complexity of strategic behavior in nearly single-peaked electorates. These are electorates that are not single-peaked but close to it according to some distance measure.#N##N#In this paper we introduce several new distance measures regarding single-peakedness. We prove that determining whether a given profile is nearly single-peaked is NP-complete in many cases. For one case we present a polynomial-time algorithm. In case the single-peaked axis is given, we show that determining the distance is always possible in polynomial time. Furthermore, we  explore the relations between the new notions introduced in this paper and existing notions from the literature.

Includes members:
  Member "on the likelihood of single peaked preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "incomplete preferences in single peaked electorates")
  Member "are there any nicely structured preference profiles nearby":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incomplete preferences in single peaked electorates")
  Member "structure in dichotomous preferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "the control complexity of r approval from the single peaked case to the general case":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "on the computation of fully proportional representation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "the complexity of fully proportional representation for single crossing electorates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  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 "clone structures in voters preferences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incomplete preferences in single peaked electorates")

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.CC(Computational Complexity)