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: "incentives quality and risks a look into the nsf proposal review pilot"
  Abstract: The National Science Foundation (NSF) will be exper- imenting with a new distributed approach to reviewing proposals, whereby a group of principal investigators (PIs) or proposers in a subfield act as reviewers for the proposals submitted by the same set of PIs. To encourage honesty, PIs chances for getting funded are tied to the quality of their reviews (with respect to the reviews provided by the entire group), in addition to the quality of their proposals. Intuitively, this approach can more fairly distribute the review workload, discourage frivolous proposal submission, and encourage high quality reviews. On the other hand, this method has already raised concerns about the integrity of the process and the possibility of strategic manipulation. In this paper, we take a closer look at three specific issues in an attempt to gai n a better understanding of the strengths and limitations of the new process beyond first impressions and anecdotal evidence . We start by considering the benefits and drawbacks of bundling the quality of PIs reviews with the scientific merit of their prop osals. We then consider the issue of collusion and favoritism. Finally, we examine whether the new process puts controversial proposals at a disadvantage. We conclude that some benefits of using revie w quality as an incentive mechanism may outweigh its drawbacks. On the other hand, even a coalition of two PIs can cause signifi cant harm to the process, as the built-in incentives are not strong enough to deter collusion. While we also confirm the common suspicion that the process is skewed toward non-controversial proposals, the more unexpected finding is that among equally controversial proposals, those of lower quality get a leg up through this process. Thus the process not only favors non-controversial proposals, but in some sense, mediocrity. We also discuss possible ways to improve this review process.

Connected through hyperedge HE-1 (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-2 (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"
    - "tuned models of peer assessment in moocs"
    - "optimal impartial selection"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-3 (induced by source paper "the peerrank method for peer assessment"):
  with members:
    - "crowdgrader crowdsourcing the evaluation of homework assignments"
    - "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)