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 "incentivizing effort and precision in peer grading"):
  Abstract: Inspired by the easily scalable solution of peer evaluation in various domains that need an ever-growing body of material to be evaluated, e.g., in journal submissions, or in evaluating proposals for public funding, peer-grading has found widespread use in Massive Open Online Coursewares (MOOCs). Current peer-grading practices often rely on the goodwill of the peer evaluators for accuracy and fairness in the evaluation, while evaluators can be competitive or energy-saver. In this paper, we consider strategic graders introduce a mechanism, TRUPEQA, that (a) uses a constant number of instructor-graded answer-scripts to quantitatively measure the accuracies of the peer graders and adjusts the finally assigned scores accordingly, and (b) penalizes deliberate under-performing. We show that this mechanism is unique in its class in satisfying certain properties desirable of a peer-grading mechanism, and in a certain setting, implements the social optima in an equilibrium. We also run a peer-grading experiment in a classroom environment and find evidence that the final assigned grades are closer to the true grades under TRUPEQA than under the peer-grading mechanism currently used in popular MOOCs.

Includes members:
  Member "incentivizing evaluation via limited access to ground truth peer prediction makes things worse":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "credible sample elicitation")
  Member "informed truthfulness in multi task peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "surrogate scoring rules")
  Member "aggregating partial rankings with applications to peer grading in massive online open courses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strategyproof peer selection using randomization partitioning and apportionment")
  Member "methods for ordinal peer grading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast and robust rank aggregation against model misspecification")
  Member "tuned models of peer assessment in moocs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "key phrase classification in complex assignments")
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "surrogate scoring rules")
  Member "mechanisms for making crowds truthful":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "report sensitive spot checking in peer grading systems")

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)