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: "of the people voting is more effective with representative candidates"
  Abstract: In light of the classic impossibility results of Arrow and Gibbard and Satterthwaite regarding voting with ordinal rules, there has been recent interest in characterizing how well common voting rules approximate the social optimum. In order to quantify the quality of approximation, it is natural to consider the candidates and voters as embedded within a common metric space, and to ask how much further the chosen candidate is from the population as compared to the socially optimal one. We use this metric preference model to explore a fundamental and timely question: does the social welfare of a population improve when candidates are representative of the population? If so, then by how much, and how does the answer depend on the complexity of the metric space? #R##N#We restrict attention to the most fundamental and common social choice setting: a population of voters, two independently drawn candidates, and a majority rule election. When candidates are not representative of the population, it is known that the candidate selected by the majority rule can be thrice as far from the population as the socially optimal one. We examine how this ratio improves when candidates are drawn independently from the population of voters. Our results are two-fold: When the metric is a line, the ratio improves from $3$ to $4-2\sqrt{2}$, roughly $1.1716$; this bound is tight. When the metric is arbitrary, we show a lower bound of $1.5$ and a constant upper bound strictly better than $2$ on the approximation ratio of the majority rule. #R##N#The positive result depends in part on the assumption that candidates are independent and identically distributed. However, we show that independence alone is not enough to achieve the upper bound: even when candidates are drawn independently, if the population of candidates can be different from the voters, then an upper bound of $2$ on the approximation is tight.

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"
    - "on the distortion value of the elections with abstention"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "communication distortion and randomness in metric voting"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "improved metric distortion for deterministic social choice rules"
    - "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"
    - "on the distortion of voting with multiple representative candidates"
    - "combinatorial auctions do need modest interaction"
    - "metric distortion of social choice rules lower bounds and fairness properties"

Connected through hyperedge HE-3 (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"
    - "on the distortion value of the elections with abstention"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "improved metric distortion for deterministic social choice rules"):
  with members:
    - "ordinal approximation for social choice matching and facility location problems given candidate positions"
    - "relating metric distortion and fairness of social choice rules"
    - "tradeoffs between information and ordinal approximation for bipartite matching"
    - "on the distortion of voting with multiple representative candidates"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "randomized social choice functions under metric preferences"
    - "on voting and facility location"

Connected through hyperedge HE-5 (induced by source paper "an analysis framework for metric voting based on lp duality"):
  with members:
    - "improved metric distortion for deterministic social choice rules"
    - "communication distortion and randomness in metric voting"
    - "a duality based unified approach to bayesian mechanism design"
    - "on the distortion of voting with multiple representative candidates"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "randomized social choice functions under metric preferences"
    - "on voting and facility location"

Connected through hyperedge HE-6 (induced by source paper "ordinal approximation for social choice matching and facility location problems given candidate positions"):
  with members:
    - "tradeoffs between information and ordinal approximation for bipartite matching"
    - "sequential deliberation for social choice"
    - "on the distortion of voting with multiple representative candidates"
    - "combinatorial secretary problems with ordinal information"
    - "truthful mechanisms for matching and clustering in an ordinal world"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "truthful facility assignment with resource augmentation an exact analysis of serial dictatorship"
    - "randomized social choice functions under metric preferences"

Connected through hyperedge HE-7 (induced by source paper "random dictators with a random referee constant sample complexity mechanisms for social choice"):
  with members:
    - "sequential deliberation for social choice"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "randomized social choice functions under metric preferences"
    - "on voting and facility location"
    - "truthful approximations to range voting"

Connected through hyperedge HE-8 (induced by source paper "sequential deliberation for social choice"):
  with members:
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "towards large scale deliberative decision making small groups and the importance of triads"
    - "randomized social choice functions under metric preferences"
    - "generalizing the single crossing property on lines and trees to intermediate preferences on median graphs"
    - "on voting and facility location"

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)