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: "crowdsourcing for participatory democracies efficient elicitation of social choice functions"
  Abstract: We present theoretical and empirical results demonstrating the usefulness of voting rules for participatory democracies. We first give algorithms which efficiently elicit \epsilon-approximations to two prominent voting rules: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. We demonstrate the approach in an experiment in Finland's recent off-road traffic law reform, observing that the total number of comparisons needed to achieve a fixed \epsilon approximation is linear in the number of ideas and that the constant is not large. #R##N#Finally, we note a few other experimental observations which support the use of voting rules for aggregation. First, we observe that rating, one of the common alternatives to ranking, manifested effects of bias in our data. Second, we show that very few of the topics lacked a Condorcet winner, one of the prominent negative results in voting. Finally, we show data hinting at a potential future direction: the use of partial rankings as opposed to pairwise comparisons to further decrease the elicitation time.

Connected through hyperedge HE-1 (induced by source paper "distributed monitoring of election winners"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "optimal random sampling from distributed streams revisited"
    - "proportional representation in vote streams"
    - "optimizing positional scoring rules for rank aggregation"
    - "fishing out winners from vote streams"
    - "estimating the margin of victory of an election using sampling"
    - "sample complexity for winner prediction in elections"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-2 (induced by source paper "the fundamentals of policy crowdsourcing"):
  with members:
    - "the contours of crowd capability"
    - "how to work a crowd developing crowd capital through crowdsourcing"
    - "experiments on crowdsourcing policy assessment"
    - "crowdsourcing the policy cycle"
    - "a computational model of crowds for collective intelligence"
    - "government and social media a case study of 31 informational world cities"
    - "the theory of crowd capital"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-3 (induced by source paper "point foundations of e democracy"):
  with members:
    - "a condorcet optimal participatory budgeting algorithm"

Connected through hyperedge HE-4 (induced by source paper "towards large scale deliberative decision making small groups and the importance of triads"):
  with members:
    - "generalizing the single crossing property on lines and trees to intermediate preferences on median graphs"
    - "notes on randomized algorithms"
    - "triadic consensus a randomized algorithm for voting in a crowd"

Connected through hyperedge HE-5 (induced by source paper "fishing out winners from vote streams"):
  with members:
    - "sample complexity for winner prediction in elections"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

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)