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: "voting with limited information and many alternatives"
  Abstract: The traditional axiomatic approach to voting is motivated by the problem of reconciling differences in subjective preferences. In contrast, a dominant line of work in the theory of voting over the past 15 years has considered a different kind of scenario, also fundamental to voting, in which there is a genuinely "best" outcome that voters would agree on if they only had enough information. This type of scenario has its roots in the classical Condorcet Jury Theorem; it includes cases such as jurors in a criminal trial who all want to reach the correct verdict but disagree in their inferences from the available evidence, or a corporate board of directors who all want to improve the company's revenue, but who have different information that favors different options. #R##N#This style of voting leads to a natural set of questions: each voter has a {\em private signal} that provides probabilistic information about which option is best, and a central question is whether a simple plurality voting system, which tabulates votes for different options, can cause the group decision to arrive at the correct option. We show that plurality voting is powerful enough to achieve this: there is a way for voters to map their signals into votes for options in such a way that --- with sufficiently many voters --- the correct option receives the greatest number of votes with high probability. We show further, however, that any process for achieving this is inherently expensive in the number of voters it requires: succeeding in identifying the correct option with probability at least $1 - \eta$ requires $\Omega(n^3 \epsilon^{-2} \log \eta^{-1})$ voters, where $n$ is the number of options and $\epsilon$ is a distributional measure of the minimum difference between the options.

Connected through hyperedge HE-1 (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"
    - "iterative local voting for collective decision making in continuous spaces"
    - "optimizing positional scoring rules for rank aggregation"

Connected through hyperedge HE-2 (induced by source paper "optimizing positional scoring rules for rank aggregation"):
  with members:
    - "how effective can simple ordinal peer grading be"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "methods for ordinal peer grading"
    - "random utility theory for social choice"
    - "common voting rules as maximum likelihood estimators"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "noisy sorting without resampling"

Connected through hyperedge HE-3 (induced by source paper "how effective can simple ordinal peer grading be"):
  with members:
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "the peerrank method for peer assessment"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "random utility theory for social choice"
    - "common voting rules as maximum likelihood estimators"

Connected through hyperedge HE-4 (induced by source paper "aggregating partial rankings with applications to peer grading in massive online open courses"):
  with members:
    - "the peerrank method for peer assessment"
    - "methods for ordinal peer grading"
    - "preference elicitation for general random utility models"
    - "tuned models of peer assessment in moocs"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "sum of us strategyproof selection from the selectors"
    - "vote elicitation complexity and strategy proofness"

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.DM(Discrete Mathematics)