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 "awareness of voter passion greatly improves the distortion of metric social choice"):
  Abstract: We develop new voting mechanisms for the case when voters and candidates are located in an arbitrary unknown metric space, and the goal is to choose a candidate minimizing social cost: the total distance from the voters to this candidate. Previous work has often assumed that only ordinal preferences of the voters are known (instead of their true costs), and focused on minimizing distortion: the quality of the chosen candidate as compared with the best possible candidate. In this paper, we instead assume that a (very small) amount of information is known about the voter preference strengths, not just about their ordinal preferences. We provide mechanisms with much better distortion when this extra information is known as compared to mechanisms which use only ordinal information. We quantify tradeoffs between the amount of information known about preference strengths and the achievable distortion. We further provide advice about which type of information about preference strengths seems to be the most useful. Finally, we conclude by quantifying the ideal candidate distortion, which compares the quality of the chosen outcome with the best possible candidate that could ever exist, instead of only the best candidate that is actually in the running.

Includes members:
  Member "improved metric distortion for deterministic social choice rules":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "the distortion of distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "manipulating elections by selecting issues":
    also connected through other hyperedges:
      (none)
  Member "peeking behind the ordinal curtain improving distortion via cardinal queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the distortion of distributed voting")
  Member "approval based elections and distortion of voting rules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "on the distortion value of the elections with abstention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")
  Member "ordinal approximation for social choice matching and facility location problems given candidate positions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "peeking behind the ordinal curtain improving distortion via cardinal queries")

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.AI(Artificial Intelligence)