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 "communication distortion and randomness in metric voting"):
  Abstract: In distortion-based analysis of social choice rules over metric spaces, one assumes that all voters and candidates are jointly embedded in a common metric space. Voters rank candidates by non-decreasing distance. The mechanism, receiving only this ordinal (comparison) information, should select a candidate approximately minimizing the sum of distances from all voters. It is known that while the Copeland rule and related rules guarantee distortion at most 5, many other standard voting rules, such as Plurality, Veto, or $k$-approval, have distortion growing unboundedly in the number $n$ of candidates. #R##N#Plurality, Veto, or $k$-approval with small $k$ require less communication from the voters than all deterministic social choice rules known to achieve constant distortion. This motivates our study of the tradeoff between the distortion and the amount of communication in deterministic social choice rules. #R##N#We show that any one-round deterministic voting mechanism in which each voter communicates only the candidates she ranks in a given set of $k$ positions must have distortion at least $\frac{2n-k}{k}$; we give a mechanism achieving an upper bound of $O(n/k)$, which matches the lower bound up to a constant. For more general communication-bounded voting mechanisms, in which each voter communicates $b$ bits of information about her ranking, we show a slightly weaker lower bound of $\Omega(n/b)$ on the distortion. #R##N#For randomized mechanisms, it is known that Random Dictatorship achieves expected distortion strictly smaller than 3, almost matching a lower bound of $3-\frac{2}{n}$ for any randomized mechanism that only receives each voter's top choice. We close this gap, by giving a simple randomized social choice rule which only uses each voter's first choice, and achieves expected distortion $3-\frac{2}{n}$.

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 "improved metric distortion for deterministic social choice rules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "approval based elections and distortion of voting rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "an analysis framework for metric voting based on lp duality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "random dictators with a random referee constant sample complexity mechanisms for social choice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "of the people voting is more effective with representative candidates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "on the distortion of voting with multiple representative candidates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "combinatorial auctions do need modest interaction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")

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)