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: "on the distortion value of the elections with abstention"
  Abstract: In Spatial Voting Theory, distortion is a measure of how good the winner is. It is proved that no deterministic voting mechanism can guarantee a distortion better than $3$, even for simple metrics such as a line. In this study, we wish to answer the following question: how does the distortion value change if we allow less motivated agents to abstain from the election? #R##N#We consider an election with two candidates and suggest an abstention model, which is a more general form of the abstention model proposed by Kirchgassner. We define the concepts of the expected winner and the expected distortion to evaluate the distortion of an election in our model. Our results fully characterize the distortion value and provide a rather complete picture of the model.

Connected through hyperedge HE-1 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries"):
  with members:
    - "improved metric distortion for deterministic social choice rules"
    - "the distortion of distributed voting"
    - "awareness of voter passion greatly improves the distortion of metric social choice"
    - "approval based elections and distortion of voting rules"
    - "an analysis framework for metric voting based on lp duality"
    - "random dictators with a random referee constant sample complexity mechanisms for social choice"
    - "ordinal approximation for social choice matching and facility location problems given candidate positions"
    - "relating metric distortion and fairness of social choice rules"

Connected through hyperedge HE-2 (induced by source paper "awareness of voter passion greatly improves the distortion of metric social choice"):
  with members:
    - "improved metric distortion for deterministic social choice rules"
    - "the distortion of distributed voting"
    - "manipulating elections by selecting issues"
    - "peeking behind the ordinal curtain improving distortion via cardinal queries"
    - "approval based elections and distortion of voting rules"
    - "random dictators with a random referee constant sample complexity mechanisms for social choice"
    - "ordinal approximation for social choice matching and facility location problems given candidate positions"
    - "relating metric distortion and fairness of social choice rules"

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)