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: "complexity of manipulation in elections with top truncated ballots"
  Abstract: In the computational social choice literature, there has been great interest in understanding how computational complexity can act as a barrier against manipulation of elections. Much of this literature, however, makes the assumption that the voters or agents specify a complete preference ordering over the set of candidates. There are many multiagent systems applications, and even real-world elections, where this assumption is not warranted, and this in turn raises the question "How hard is it to manipulate elections if the agents reveal only partial preference orderings?" It is this question we try to address in this paper. In particular, we look at the weighted manipulation problem -- both constructive and destructive manipulation -- when the voters are allowed to specify any top-truncated ordering over the set of candidates. We provide general results for all scoring rules, for elimination versions of all scoring rules, for the plurality with runoff rule, for a family of election systems known as Copeland$^{\alpha}$, and for the maximin protocol. Finally, we also look at the impact on complexity of manipulation when there is uncertainty about the non-manipulators' votes.

Connected through hyperedge HE-1 (induced by source paper "modeling single peakedness for votes with ties"):
  with members:
    - "computational aspects of nearly single peaked electorates"
    - "single peaked consistency for weak orders is easy"
    - "reinstating combinatorial protections for manipulation and bribery in single peaked and nearly single peaked electorates"
    - "complexity of manipulative actions when voting with ties"
    - "the computational impact of partial votes on strategic voting"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "the shield that never was societies with single peaked preferences are more open to manipulation and control"
    - "dichotomy for voting systems"

Connected through hyperedge HE-2 (induced by source paper "reinstating combinatorial protections for manipulation and bribery in single peaked and nearly single peaked electorates"):
  with members:
    - "computational aspects of nearly single peaked electorates"
    - "complexity of manipulative actions when voting with ties"
    - "the computational impact of partial votes on strategic voting"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "the shield that never was societies with single peaked preferences are more open to manipulation and control"
    - "eliciting single peaked preferences using comparison queries"
    - "how hard is bribery in elections"

Connected through hyperedge HE-3 (induced by source paper "complexity of manipulative actions when voting with ties"):
  with members:
    - "complexity of manipulation in elections with partial votes"
    - "the computational impact of partial votes on strategic voting"
    - "weighted electoral control"
    - "the shield that never was societies with single peaked preferences are more open to manipulation and control"
    - "how hard is bribery in elections"
    - "dichotomy for voting systems"
    - "anyone but him the complexity of precluding an alternative"

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.GT(Computer Science and Game Theory)