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: "a near optimal mechanism for impartial selection"
  Abstract: We examine strategy-proof elections to select a winner amongst a set of agents, each of whom cares only about winning. This impartial selection problem was introduced independently by Holzman and Moulin and Alon et al. Fisher and Klimm showed that the permutation mechanism is impartial and $1/2$-optimal, that is, it selects an agent who gains, in expectation, at least half the number of votes of most popular agent. Furthermore, they showed the mechanism is $7/12$-optimal if agents cannot abstain in the election. We show that a better guarantee is possible, provided the most popular agent receives at least a large enough, but constant, number of votes. Specifically, we prove that, for any $\epsilon>0$, there is a constant $N_{\epsilon}$ (independent of the number $n$ of voters) such that, if the maximum number of votes of the most popular agent is at least $N_{\epsilon}$ then the permutation mechanism is $(\frac{3}{4}-\epsilon)$-optimal. This result is tight. #R##N#Furthermore, in our main result, we prove that near-optimal impartial mechanisms exist. In particular, there is an impartial mechanism that is $(1-\epsilon)$-optimal, for any $\epsilon>0$, provided that the maximum number of votes of the most popular agent is at least a constant $M_{\epsilon}$.

Connected through hyperedge HE-1 (induced by source paper "impartial selection with additive approximation guarantees"):
  with members:
    - "incentive compatible diffusion"
    - "strategyproof linear regression in high dimensions"
    - "optimal impartial selection"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-2 (induced by source paper "incentive compatible classification"):
  with members:
    - "optimal impartial selection"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-3 (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"
    - "optimizing positional scoring rules for rank aggregation"
    - "strategyproof peer selection using randomization partitioning and apportionment"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "incentive design in peer review rating and repeated endogenous matching"
    - "tuned models of peer assessment in moocs"

Connected through hyperedge HE-4 (induced by source paper "strategyproof peer selection using randomization partitioning and apportionment"):
  with members:
    - "friend based ranking"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "design and analysis of the nips 2016 review process"
    - "how effective can simple ordinal peer grading be"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "the peerrank method for peer assessment"
    - "crowdgrader crowdsourcing the evaluation of homework assignments"
    - "incentives quality and risks a look into the nsf proposal review pilot"

Connected through hyperedge HE-5 (induced by source paper "strategyproof peer selection"):
  with members:
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "the peerrank method for peer assessment"
    - "crowdgrader crowdsourcing the evaluation of homework assignments"
    - "incentives quality and risks a look into the nsf proposal review pilot"
    - "tuned models of peer assessment in moocs"
    - "optimal impartial selection"
    - "sum of us strategyproof selection from the selectors"

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)