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: "where are the hard manipulation problems"
  Abstract: One possible escape from the Gibbard-Satterthwaite theorem is computational complexity. For example, it is NP-hard to compute if the STV rule can be manipulated. However, there is increasing concern that such results may not re ect the difficulty of manipulation in practice. In this tutorial, I survey recent results in this area.

Connected through hyperedge HE-1 (induced by source paper "lie on the fly strategic voting in an iterative preference elicitation process"):
  with members:
    - "cognitive hierarchy and voting manipulation"
    - "online prediction with selfish experts"
    - "a local dominance theory of voting equilibria"
    - "the computational impact of partial votes on strategic voting"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "dominating manipulations in voting with partial information"
    - "equilibria of plurality voting with abstentions"

Connected through hyperedge HE-2 (induced by source paper "deceased organ matching in australia"):
  with members:
    - "manipulating the probabilistic serial rule"
    - "online fair division analysing a food bank problem"
    - "welfare of sequential allocation mechanisms for indivisible goods"
    - "allocation in practice"
    - "possible and necessary allocations via sequential mechanisms"
    - "fair assignment of indivisible objects under ordinal preferences"
    - "a social welfare optimal sequential allocation procedure"
    - "an empirical study of the manipulability of single transferable voting"

Connected through hyperedge HE-3 (induced by source paper "on the likelihood of single peaked preferences"):
  with members:
    - "computational aspects of nearly single peaked electorates"
    - "the one dimensional euclidean domain finitely many obstructions are not enough"
    - "are there any nicely structured preference profiles nearby"
    - "on the computation of fully proportional representation"
    - "the complexity of manipulative attacks in nearly single peaked electorates"
    - "clone structures in voters preferences"
    - "an empirical study of the manipulability of single transferable voting"
    - "the shield that never was societies with single peaked preferences are more open to manipulation and control"

Connected through hyperedge HE-4 (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"
    - "detecting possible manipulators in elections"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "complexity dichotomies for unweighted scoring rules"):
  with members:
    - "a control dichotomy for pure scoring rules"
    - "weighted electoral control"
    - "control complexity in bucklin fallback and plurality voting an experimental approach"
    - "taking the final step to a full dichotomy of the possible winner problem in pure scoring rules"
    - "complexity of and algorithms for borda manipulation"
    - "towards a dichotomy of finding possible winners in elections based on scoring rules"
    - "how hard is bribery in elections"

Connected through hyperedge HE-6 (induced by source paper "small representations of big kidney exchange graphs"):
  with members:
    - "position indexed formulations for kidney exchange"

Connected through hyperedge HE-7 (induced by source paper "generating single peaked votes"):
  with members:
    - "an empirical study of the manipulability of single transferable voting"
    - "the shield that never was societies with single peaked preferences are more open to manipulation and control"
    - "where are the really hard manipulation problems the phase transition in manipulating the veto rule"
    - "eliciting single peaked preferences using comparison queries"

Connected through hyperedge HE-8 (induced by source paper "more natural models of electoral control by partition"):
  with members:
    - "elections with few voters candidate control can be easy"
    - "combinatorial voter control in elections"
    - "weighted electoral control"
    - "search versus decision for election manipulation problems"
    - "an atypical survey of typical case heuristic algorithms"
    - "normalized range voting broadly resists control"
    - "sincere strategy preference based approval voting fully resists constructive control and broadly resists destructive control"
    - "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.AI(Artificial Intelligence)