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: "how hard is it to control an election by breaking ties"
  Abstract: We study the computational complexity of controlling the result of an election by breaking ties strategically. This problem is equivalent to the problem of deciding the winner of an election under parallel universes tie-breaking. When the chair of the election is only asked to break ties to choose between one of the co-winners, the problem is trivially easy. However, in multi-round elections, we prove that it can be NP-hard for the chair to compute how to break ties to ensure a given result. Additionally, we show that the form of the tie-breaking function can increase the opportunities for control. Indeed, we prove that it can be NP-hard to control an election by breaking ties even with a two-stage 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"
    - "detecting possible manipulators in elections"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"

Connected through hyperedge HE-2 (induced by source paper "heuristics in multi winner approval voting"):
  with members:
    - "modeling peoples voting behavior with poll information"
    - "incentivizing high quality crowdwork"
    - "a local dominance theory of voting equilibria"
    - "computational aspects of multi winner approval voting"

Connected through hyperedge HE-3 (induced by source paper "heuristic strategies in uncertain approval voting environments"):
  with members:
    - "modeling peoples voting behavior with poll information"
    - "heuristics in multi winner approval voting"
    - "a local dominance theory of voting equilibria"
    - "computational aspects of multi winner approval voting"

Connected through hyperedge HE-4 (induced by source paper "the schulze method of voting"):
  with members:
    - "approval based elections and distortion of voting rules"
    - "a note on the complexity of manipulating weighted schulze voting"
    - "the expanding approvals rule improving proportional representation and monotonicity"
    - "the flow network method"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "fault tolerant entity resolution with the crowd"
    - "choosing by means of approval preferential voting the revised approval choice"
    - "schulze and ranked pairs voting are fixed parameter tractable to bribe manipulate and control"

Connected through hyperedge HE-5 (induced by source paper "on the hardness of bribery variants in voting with cp nets"):
  with members:
    - "campaign management under approval driven voting rules"
    - "k best enumeration"
    - "towards a dichotomy for the possible winner problem in elections based on scoring rules"
    - "approximation algorithms for campaign management"
    - "multivariate complexity analysis of swap bribery"
    - "how hard is bribery in elections"
    - "a richer understanding of the complexity of election systems"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

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)