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: "optimizing positional scoring rules for rank aggregation"
  Abstract: Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical viewpoint and present positive and negative complexity results and, furthermore, complement our theoretical findings with experiments on real-world and synthetic data.

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"
    - "learning mixtures of plackett luce models"

Connected through hyperedge HE-2 (induced by source paper "quantifying consensus of rankings based on q support patterns"):
  with members:
    - "maximal rank correlation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "maximal rank correlation"
    - "a weighted correlation index for rankings with ties"
    - "a family of rank similarity measures based on maximized effectiveness difference"

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"
    - "fishing out winners from vote streams"
    - "estimating the margin of victory of an election using sampling"
    - "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 "on strategyproof conference peer review"):
  with members:
    - "does my rebuttal matter insights from a major nlp conference"
    - "choosing how to choose papers"
    - "design and analysis of the nips 2016 review process"
    - "strategyproof peer selection using randomization partitioning and apportionment"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "a near optimal mechanism for impartial selection"
    - "incentive design in peer review rating and repeated endogenous matching"
    - "tuned models of peer assessment in moocs"

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.GT(Computer Science and Game Theory)