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 for truthful peer grading"
  Abstract: Peer grading systems work well only if users have incentives to grade truthfully. An example of non-truthful grading, that we observed in classrooms, consists in students assigning the maximum grade to all submissions. With a naive grading scheme, such as averaging the assigned grades, all students would receive the maximum grade. In this paper, we develop three grading schemes that provide incentives for truthful peer grading. In the first scheme, the instructor grades a fraction p of the submissions, and penalizes students whose grade deviates from the instructor grade. We provide lower bounds on p to ensure truthfulness, and conclude that these schemes work only for moderate class sizes, up to a few hundred students. To overcome this limitation, we propose a hierarchical extension of this supervised scheme, and we show that it can handle classes of any size with bounded (and little) instructor work, and is therefore applicable to Massive Open Online Courses (MOOCs). Finally, we propose unsupervised incentive schemes, in which the student incentive is based on statistical properties of the grade distribution, without any grading required by the instructor. We show that the proposed unsupervised schemes provide incentives to truthful grading, at the price of being possibly unfair to individual students.

Connected through hyperedge HE-1 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "density functional estimators with k nearest neighbor bandwidths"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-2 (induced by source paper "surrogate scoring rules"):
  with members:
    - "deep bayesian trust a dominant and fair incentive mechanism for crowd"
    - "water from two rocks maximizing the mutual information"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-3 (induced by source paper "credible sample elicitation by deep learning for deep learning"):
  with members:
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"

Connected through hyperedge HE-4 (induced by source paper "report sensitive spot checking in peer grading systems"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-5 (induced by source paper "a budget feasible peer graded mechanism for iot based crowdsourcing"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "a budget feasible mechanism for hiring doctors in e healthcare"
    - "providing long term participation incentive in participatory sensing"
    - "online assignment of heterogeneous tasks in crowdsourcing markets"
    - "on cost effective incentive mechanisms in microtask crowdsourcing"
    - "budget feasible mechanisms"

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)