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: "estimating the margin of victory of an election using sampling"
  Abstract: The margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections. #R##N#More formally, we introduce the \textsc{$(c, \epsilon, \delta)$--Margin of Victory} problem, where given an election $\mathcal{E}$ on $n$ voters, the goal is to estimate the margin of victory $M(\mathcal{E})$ of $\mathcal{E}$ within an additive factor of $c MoV(\mathcal{E})+\epsilon n$. We study the \textsc{$(c, \epsilon, \delta)$--Margin of Victory} problem for many commonly used voting rules including scoring rules, approval, Bucklin, maximin, and Copeland$^{\alpha}.$ We observe that even for the voting rules for which computing the margin of victory is NP-Hard, there may exist efficient sampling based algorithms, as observed in the cases of maximin and Copeland$^{\alpha}$ voting rules.

Connected through hyperedge HE-1 (induced by source paper "minimizing margin of victory for fair political and educational districting"):
  with members:
    - "computing the margin of victory in preferential parliamentary elections"
    - "more natural models of electoral control by partition"

Connected through hyperedge HE-2 (induced by source paper "refining tournament solutions via margin of victory"):
  with members:
    - "query complexity of tournament solutions"
    - "manipulating tournaments in cup and round robin competitions"

Connected through hyperedge HE-3 (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"
    - "sample complexity for winner prediction in elections"
    - "crowdsourcing for participatory democracies efficient elicitation of social choice functions"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-4 (induced by source paper "algorithms for destructive shift bribery"):
  with members:
    - "robustness among multiwinner voting rules"
    - "voting and bribing in single exponential time"
    - "on the hardness of bribery variants in voting with cp nets"
    - "prices matter for the parameterized complexity of shift bribery"
    - "campaign management under approval driven voting rules"
    - "properties of multiwinner voting rules"
    - "complexity of manipulation bribery and campaign management in bucklin and fallback voting"
    - "approximation algorithms for campaign management"

Connected through hyperedge HE-5 (induced by source paper "surprise in elections"):
  with members:
    - "the complexity of manipulative attacks in nearly single peaked electorates"

Connected through hyperedge HE-6 (induced by source paper "quantifying filter bubbles analyzing surprise in elections"):
  with members:
    - "surprise in elections"

Connected through hyperedge HE-7 (induced by source paper "robustness among multiwinner voting rules"):
  with members:
    - "what do multiwinner voting rules do an experiment over the two dimensional euclidean domain"
    - "very hard electoral control problems"
    - "distributed monitoring of election winners"
    - "committee scoring rules axiomatic characterization and hierarchy"
    - "algorithms for destructive shift bribery"
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "towards computing victory margins in stv elections"
    - "voting and bribing in single exponential time"

Connected through hyperedge HE-8 (induced by source paper "manipulative elicitation a new attack on elections with incomplete preferences"):
  with members:
    - "on the exact amount of missing information that makes finding possible winners hard"
    - "elicitation for preferences single peaked on trees"
    - "preference elicitation for single crossing domain"
    - "query complexity of tournament solutions"
    - "complexity of manipulation with partial information in voting"
    - "frugal bribery in voting"
    - "detecting possible manipulators in elections"
    - "kernelization complexity of possible winner and coalitional manipulation problems in voting"

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.AI(Artificial Intelligence)