human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "a parameterized perspective on protecting elections"):
  with members:
    - "optimal bribery in voting"
    - "manipulative elicitation a new attack on elections with incomplete preferences"
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "complexity of manipulation with partial information in voting"
    - "frugal bribery in voting"
    - "elections with few voters candidate control can be easy"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"
    - "more natural models of electoral control by partition"

Connected through hyperedge HE-2 (induced by source paper "manipulative elicitation a new attack on elections with incomplete preferences"):
  with members:
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "elicitation for preferences single peaked on trees"
    - "preference elicitation for single crossing domain"
    - "query complexity of tournament solutions"
    - "complexity of manipulation with partial information in voting"
    - "estimating the margin of victory of an election using sampling"
    - "frugal bribery in voting"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"

Connected through hyperedge HE-3 (induced by source paper "complexity of manipulation with partial information in voting"):
  with members:
    - "manipulative elicitation a new attack on elections with incomplete preferences"
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "complexity of manipulation in elections with partial votes"
    - "frugal bribery in voting"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"
    - "the computational impact of partial votes on strategic voting"
    - "possible and necessary winner problem in social polls"
    - "asymptotic collusion proofness of voting rules the case of large number of candidates"

Question: Please tell me which class the center node 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)