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: "optimal impartial selection"
  Abstract: We study the problem of selecting a member of a set of agents based on impartial nominations by agents from that set. The problem was studied previously by Alon et al. and Holzman and Moulin and has important applications in situations where representatives are selected from within a group or where publishing or funding decisions are made based on a process of peer review. Our main result concerns a randomized mechanism that in expectation awards the prize to an agent with at least half the maximum number of nominations. Subject to impartiality, this is best possible.

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"
    - "a near optimal mechanism for impartial selection"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-2 (induced by source paper "sequential voting with confirmation network"):
  with members:
    - "equilibria of plurality voting with abstentions"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-3 (induced by source paper "incentive compatible classification"):
  with members:
    - "a near optimal mechanism for impartial selection"
    - "sum of us strategyproof selection from the selectors"

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"
    - "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"
    - "a near optimal mechanism for impartial selection"
    - "incentive design in peer review rating and repeated endogenous matching"

Connected through hyperedge HE-5 (induced by source paper "strategyproof linear regression in high dimensions"):
  with members:
    - "strategic classification from revealed preferences"
    - "learning from untrusted data"
    - "truthful linear regression"
    - "linear regression as a non cooperative game"

Connected through hyperedge HE-6 (induced by source paper "incentive compatible diffusion"):
  with members:
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-7 (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"
    - "a near optimal mechanism for impartial selection"
    - "the peerrank method for peer assessment"
    - "crowdgrader crowdsourcing the evaluation of homework assignments"

Connected through hyperedge HE-8 (induced by source paper "strategyproof peer selection"):
  with members:
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "a near optimal mechanism for impartial selection"
    - "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"
    - "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)