human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distributed monitoring of election winners"):
  Abstract: We consider distributed elections, where there is a center and $k$ sites. In such distributed elections, each voter has preferences over some set of candidates, and each voter is assigned to exactly one site such that each site is aware only of the voters assigned to it. The center is able to directly communicate with all sites. We are interested in designing communication-efficient protocols, allowing the center to maintain a candidate which, with arbitrarily high probability, is guaranteed to be a winner, or at least close to being a winner. We consider various single-winner voting rules, such as variants of Approval voting and scoring rules, tournament-based voting rules, and several round-based voting rules. For the voting rules we consider, we show that, using communication which is logarithmic in the number of voters, it is possible for the center to maintain such approximate winners; that is, upon a query at any time the center can immediately return a candidate which is guaranteed to be an approximate winner with high probability. We complement our protocols with lower bounds. Our results are theoretical in nature and relate to various scenarios, such as aggregating customer preferences in online shopping websites or supermarket chains and collecting votes from different polling stations of political elections.

Includes members:
  Member "comparing election methods where each voter ranks only few candidates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating voting rules from truncated ballots")
  Member "optimal random sampling from distributed streams revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication efficient weighted reservoir sampling from fully distributed data streams")
  Member "proportional representation in vote streams":
    also connected through other hyperedges:
      (none)
  Member "optimizing positional scoring rules for rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "fishing out winners from vote streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "proportional representation in vote streams")
  Member "estimating the margin of victory of an election using sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimizing margin of victory for fair political and educational districting")
  Member "sample complexity for winner prediction in elections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sybil resilient social choice with partial participation")
  Member "crowdsourcing for participatory democracies efficient elicitation of social choice functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the fundamentals of policy crowdsourcing")

Question: Please tell me which class the source paper 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)