human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on strategyproof conference peer review"):
  Abstract: We consider peer review in a conference setting where there is typically an overlap between the set of reviewers and the set of authors. This overlap can incentivize strategic reviews to influence the final ranking of one's own papers. In this work, we address this problem through the lens of social choice, and present a theoretical framework for strategyproof and efficient peer review. We first present and analyze an algorithm for reviewer-assignment and aggregation that guarantees strategyproofness and a natural efficiency property called unanimity, when the authorship graph satisfies a simple property. Our algorithm is based on the so-called partitioning method, and can be thought as a generalization of this method to conference peer review settings. We then empirically show that the requisite property on the authorship graph is indeed satisfied in the ICLR-17 submission data, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review. Finally, we complement our positive results with negative theoretical results where we prove that under various ways of strengthening the requirements, it is impossible for any algorithm to be strategyproof and efficient.

Includes members:
  Member "does my rebuttal matter insights from a major nlp conference":
    also connected through other hyperedges:
      (none)
  Member "choosing how to choose papers":
    also connected through other hyperedges:
      (none)
  Member "design and analysis of the nips 2016 review process":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accounting for the neglected dimensions of ai progress")
  Member "optimizing positional scoring rules for rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "strategyproof peer selection using randomization partitioning and apportionment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity stability properties of mixed games and dynamic algorithms and learning in the sharing economy")
  Member "estimation from pairwise comparisons sharp minimax bounds with topology dependence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "a near optimal mechanism for impartial selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "impartial selection with additive approximation guarantees")
  Member "incentive design in peer review rating and repeated endogenous matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "repeated matching mechanism design with moral hazard and adverse selection")

Question: Please tell me which class the source paper 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)