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: "what do multiwinner voting rules do an experiment over the two dimensional euclidean domain"
  Abstract: We visualize aggregate outputs of popular multiwinner voting rules--SNTV, STV, Bloc, k-Borda, Monroe, Chamberlin--Courant, and HarmonicBorda--for elections generated according to the two-dimensional Euclidean model. We consider three applications of multiwinner voting, namely, parliamentary elections, portfolio/movie selection, and shortlisting, and use our results to understand which of our rules seem to be best suited for each application. In particular, we show that STV (one of the few nontrivial rules used in real high-stake elections) exhibits excellent performance, whereas the Bloc rule (also often used in practice) performs poorly.

Connected through hyperedge HE-1 (induced by source paper "favorite candidate voting for eliminating the least popular candidate in metric spaces"):
  with members:
    - "improved metric distortion for deterministic social choice rules"
    - "awareness of voter passion greatly improves the distortion of metric social choice"
    - "ordinal approximation for social choice matching and facility location problems given candidate positions"
    - "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-2 (induced by source paper "committee scoring rules axiomatic characterization and hierarchy"):
  with members:
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "monotonicity axioms in approval based multi winner voting rules"
    - "multiwinner elections with diversity constraints"
    - "consistent approval based multi winner rules"
    - "multiwinner voting with fairness constraints"
    - "group fairness in multiwinner voting"
    - "robustness among multiwinner voting rules"
    - "proportional approval voting harmonic k median and negative association"

Connected through hyperedge HE-3 (induced by source paper "a quantitative analysis of multi winner rules"):
  with members:
    - "proportionality and the limits of welfarism"
    - "group fairness in committee selection"
    - "fair allocation of indivisible public goods"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "consistent approval based multi winner rules"
    - "robustness among multiwinner voting rules"
    - "what do we elect committees for a voting committee model for multi winner rules"
    - "metric distortion of social choice rules lower bounds and fairness properties"

Connected through hyperedge HE-4 (induced by source paper "robustness among multiwinner voting rules"):
  with members:
    - "very hard electoral control problems"
    - "distributed monitoring of election winners"
    - "committee scoring rules axiomatic characterization and hierarchy"
    - "algorithms for destructive shift bribery"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "towards computing victory margins in stv elections"
    - "voting and bribing in single exponential time"
    - "axiomatic characterization of committee scoring 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)