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: "iterative local voting for collective decision making in continuous spaces"
  Abstract: Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a algorithm called {\sc Iterative Local Voting} for collective decision-making in this setting. In this algorithm, voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as defined by a ball in some chosen norm, with the size of the ball shrinking at a specified rate. #R##N#We first prove the convergence of this algorithm under appropriate choices of neighborhoods to Pareto optimal solutions with desirable fairness properties in certain natural settings: when the voters' utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution. #R##N#We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 2,000 workers, employing neighborhoods defined by $\mathcal{L}^1, \mathcal{L}^2$ and $\mathcal{L}^\infty$ balls. We make several observations that inform future implementations of such a procedure.

Connected through hyperedge HE-1 (induced by source paper "participatory budgeting models and approaches"):
  with members:
    - "truthful aggregation of budget proposals"
    - "markets for public decision making"
    - "fair allocation of indivisible public goods"
    - "proportionally representative participatory budgeting axioms and algorithms"
    - "fair mixing the case of dichotomous preferences"
    - "the impossibility of extending random dictatorship to weak preferences"
    - "the core of the participatory budgeting problem"
    - "justified representation in approval based committee voting"

Connected through hyperedge HE-2 (induced by source paper "who is in your top three optimizing learning in elections with many candidates"):
  with members:
    - "truthful aggregation of budget proposals"
    - "a framework for approval based budgeting methods"
    - "designing optimal binary rating systems"
    - "a quantitative analysis of multi winner rules"
    - "designing informative rating systems for online platforms evidence from two experiments"
    - "consistent approval based multi winner rules"
    - "optimizing positional scoring rules for rank aggregation"
    - "learning mixtures of plackett luce models"

Connected through hyperedge HE-3 (induced by source paper "truthful aggregation of budget proposals"):
  with members:
    - "markets for public decision making"
    - "proportionally representative participatory budgeting axioms and algorithms"
    - "fair mixing the case of dichotomous preferences"
    - "the core of the participatory budgeting problem"
    - "strategyproof approximation mechanisms for location on networks"

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.MA(Multiagent Systems)