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 "detecting possible manipulators in elections"):
  Abstract: Manipulation is a problem of fundamental importance in the context of voting in which the voters exercise their votes strategically instead of voting honestly to prevent selection of an alternative that is less preferred. The Gibbard-Satterthwaite theorem shows that there is no strategy-proof voting rule that simultaneously satisfies certain combinations of desirable properties. Researchers have attempted to get around the impossibility results in several ways such as domain restriction and computational hardness of manipulation. However these approaches have been shown to have limitations. Since prevention of manipulation seems to be elusive, an interesting research direction therefore is detection of manipulation. Motivated by this, we initiate the study of detection of possible manipulators in an election. #R##N#We formulate two pertinent computational problems - Coalitional Possible Manipulators (CPM) and Coalitional Possible Manipulators given Winner (CPMW), where a suspect group of voters is provided as input to compute whether they can be a potential coalition of possible manipulators. In the absence of any suspect group, we formulate two more computational problems namely Coalitional Possible Manipulators Search (CPMS), and Coalitional Possible Manipulators Search given Winner (CPMSW). We provide polynomial time algorithms for these problems, for several popular voting rules. For a few other voting rules, we show that these problems are in NP-complete. We observe that detecting manipulation maybe easy even when manipulation is hard, as seen for example, in the case of the Borda voting rule.

Includes members:
  Member "asymptotic collusion proofness of voting rules the case of large number of candidates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complexity of manipulation with partial information in voting")
  Member "coalitional manipulation for schulze s rule":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a note on the complexity of manipulating weighted schulze voting")
  Member "complexity of manipulation bribery and campaign management in bucklin and fallback voting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal bribery in voting")
  Member "weighted electoral control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selecting voting locations for fun and profit")
  Member "determining possible and necessary winners under common voting rules given partial orders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the complexity of possible winners on partial chains")
  Member "manipulation of nanson s and baldwin s rules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how hard is it to control an election by breaking ties")
  Member "complexity of and algorithms for borda manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the complexity of possible winners on partial chains")
  Member "is computational complexity a barrier to manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "election attacks with few candidates")

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.MA(Multiagent Systems)