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: "reinstating combinatorial protections for manipulation and bribery in single peaked and nearly single peaked electorates"
  Abstract: Understanding when and how computational complexity can be used to protect elections against different manipulative actions has been a highly active research area over the past two decades. A recent body of work, however, has shown that many of the NP-hardness shields, previously obtained, vanish when the electorate has single-peaked or nearly single-peaked preferences. In light of these results, we investigate whether it is possible to reimpose NP-hardness shields for such electorates by allowing the voters to specify partial preferences instead of insisting they cast complete ballots. In particular, we show that in single-peaked and nearly single-peaked electorates, if voters are allowed to submit top-truncated ballots, then the complexity of manipulation and bribery for many voting rules increases from being in P to being NP-complete.

Connected through hyperedge HE-1 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences"):
  with members:
    - "computational aspects of nearly single peaked electorates"
    - "structure in dichotomous preferences"
    - "the control complexity of r approval from the single peaked case to the general case"
    - "on the computation of fully proportional representation"
    - "the complexity of fully proportional representation for single crossing electorates"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "multimode control attacks on elections"
    - "the complexity of manipulating k approval elections"

Connected through hyperedge HE-2 (induced by source paper "incomplete preferences in single peaked electorates"):
  with members:
    - "on the likelihood of single peaked preferences"
    - "computational aspects of nearly single peaked electorates"
    - "recognising multidimensional euclidean preferences"
    - "modeling single peakedness for votes with ties"
    - "single peaked consistency for weak orders is easy"
    - "the one dimensional euclidean domain finitely many obstructions are not enough"
    - "complexity of manipulative actions when voting with ties"
    - "are there any nicely structured preference profiles nearby"

Connected through hyperedge HE-3 (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"
    - "complexity of manipulative actions when voting with ties"
    - "complexity of manipulation in elections with top truncated ballots"
    - "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"

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)